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Strength characteristics of industrial melts of multicomponent titanium alloys with .03 to .075 aluminum content
Four highly experienced Air Force pilots each flew four simulated flight scenarios. Two scenarios required a great deal of aircraft maneuvering. The other two scenarios involved less maneuvering, but required remembering a number of items. All scenarios were designed to be equaly challenging. Pilot's Subjective Ratings for Activity-level, Complexity, Difficulty, Stress, and Workload were higher for the manuevering scenarios than the memory scenarios. At a moderate workload level, keeping the pilots active resulted in better aircraft control. When required to monitor and remember items, aircraft control tended to decrease. Pilots tended to weigh information about the spatial positioning and performance of their aircraft more heavily than other items.
NASA's Human Exploration Plans: A primary goal of all major space faring nations is to explore space: from the Earth with telescopes, with robotic probes and space telescopes, and with humans. For the US National Aeronautics and Space Administration (NASA), this pursuit is captured in three important strategic goals: 1. Ascertain the content, origin, and evolution of the solar system and the potential for life elsewhere, 2. Extend and sustain human activities across the solar system (especially the surface of Mars), and 3. Create innovative new space technologies for exploration, science, and economic future. While specific missions and destinations are still being discussed as to what comes first, it is imperative for NASA that it foster the development and implementation of new technologies and approaches that make space exploration affordable and sustainable. Critical to achieving affordable and sustainable human exploration beyond low Earth orbit (LEO) is the development of technologies and systems to identify, extract, and use resources in space instead of bringing everything from Earth. To reduce the development and implementation costs for space resource utilization, often called In Situ Resource Utilization (ISRU), it is imperative to work with terrestrial mining companies to spin-in/spin-off technologies and capabilities, and space mining companies to expand our economy beyond Earth orbit. In the last two years, NASA has focused on developing and implementing a sustainable human space exploration program with the ultimate goal of exploring the surface of Mars with humans. The plan involves developing technology and capability building blocks critical for sustained exploration starting with the Space Launch System (SLS) and Orion crew spacecraft and utilizing the International Space Station as a springboard into the solar system. The evolvable plan develops and expands human exploration in phases starting with missions that are reliant on Earth, to performing ever more challenging and longer duration missions in cis-lunar space and beyond, to eventually being independent from Earth. The goal is no longer just to reach a destination, but to enable people to work, learn, operate, and live safely beyond the Earth for extended periods of time, ultimately in ways that are more sustainable and even indefinite.
A primary goal of all major space faring nations is to explore space: from the Earth with telescopes, with robotic probes and space telescopes, and with humans. For the US National Aeronautics and Space Administration (NASA), this pursuit is captured in three important strategic goals: 1. Ascertain the content, origin, and evolution of the solar system and the potential for life elsewhere, 2. Extend and sustain human activities across the solar system (especially the surface of Mars), and 3. Create innovative new space technologies for exploration, science, and economic future. While specific missions and destinations are still being discussed as to what comes first, it is imperative for NASA that it foster the development and implementation of new technologies and approaches that make space exploration affordable and sustainable. Critical to achieving affordable and sustainable human exploration beyond low Earth orbit (LEO) is the development of technologies and systems to identify, extract, and use resources in space instead of bringing everything from Earth. To reduce the development and implementation costs for space resource utilization, often called In Situ Resource Utilization (ISRU), it is imperative to work with terrestrial mining companies to spin-in/spin-off technologies and capabilities, and space mining companies to expand our economy beyond Earth orbit. In the last two years, NASA has focused on developing and implementing a sustainable human space exploration program with the ultimate goal of exploring the surface of Mars with humans. The plan involves developing technology and capability building blocks critical for sustained exploration starting with the Space Launch System (SLS) and Orion crew spacecraft and utilizing the International Space Station as a springboard into the solar system. The evolvable plan develops and expands human exploration in phases starting with missions that are reliant on Earth, to performing ever more challenging and longer duration missions in cis-lunar space and beyond, to eventually being independent from Earth. The goal is no longer just to reach a destination, but to enable people to work, learn, operate, and live safely beyond the Earth for extended periods of time, ultimately in ways that are more sustainable and even indefinite.
Abstract not provided.
This report describes the development and testing of a new method for extrapolating short-term creep rupture test data to predict long-term rupture strength. The goal of this work is to reduce the time required to qualify new materials for nuclear service by reducing the lead time required for dedicated, long-term material testing to establish key long-term material properties. The new approach described here uses a physics-based model to predict the long-term creep rupture strength of 316H stainless steel using only short-term test data. The key idea is to use Bayesian inference to find the statistical distribution of the model parameters that best explain the short-term rupture data. Because the model is physics-based these parameters are all microstructural quantities that can be measured through detailed material characterization experiments. The Bayesian prior distributions provide a means for incorporating this characterization data into the final model to improve the accuracy of the long-term model predictions. However, where such data is not available the process still produces an accurate model based on an uniformed prior. Our hypothesis is that this approach more accurately extrapolates the short-term test data when compared to current, empirical methods. The report proves this hypothesis using actual long-term rupture data available for 316H, including tests with rupture times greater than 200,000 hours. The general approach developed here could be applied to other materials and other time-dependent material properties. Applying this new technique to develop long-term qualified material properties, potentially in conjunction with other accelerated qualification approaches like staggered qualification test programs, could greatly reduce the time required to qualify new materials for nuclear service.
The current literature on climate services for farmers predominantly focuses on seasonal forecasts, with an assumption that longer-term climate projections may not be suitable for informing farming decisions. In this paper, we explore whether certain types of long-term climate projections may be useful for some specific types of farming decisions. Through interviews with almond tree crop farmers and farm advisors in California, we examine how farmers perceive the utility and accuracy levels of long-term climate projections and identify the types of projections that they may find useful. The interviews revealed that farmers often perceive long-term climate projections as an extension of weather forecasts, which can lead to their initial skepticism of the utility of such information. However, we also found that when farmers were presented with long-term trends or shifts in crop-specific agroclimatic metrics (such as chill hours or summer heat), they immediately perceived these as valuable for their decision-making. Hence, the manner in which long-term projections are framed, presented, and discussed with farmers can heavily influence their perception of the potential utility of such projections. The iterative conversations as part of the exploratory interview questions, served as a tool for “ joint construction of meaning” of complex and ambiguous terms such as “long-term climate projections,” “long-term decisions” and “uncertainty.” This in-turn supported a joint identification (and understanding) of the types of information that can potentially be useful for on-farm adaptive decisions, where the farmer and the interviewer both improvise and iterate to find the best types of projections that fit specific decision-contexts. Overall, this research identifies both the types of long-term climate information that farmers may consider useful, and the engagement processes that are able to effectively elicit farmers' long-term information needs.
An understanding of the long-term effectiveness of remediation technologies is central to sustainable environmental cleanup. Long-term experiments are valuable for reducing uncertainty and predicting remediation outcomes at scales required for regulatory compliance. However, these types of tests can be costly and challenging to interpret. Therefore, contaminated sites often rely on short-term laboratory experiments that may not account for the potentially significant effects of gradual, time- dependent processes governing contaminant retention, release, and species transformation. For example, short-term lab experiments (from months to a year) conducted with sediments from the unsaturated and saturated zones at the Hanford Site play an important role in initial evaluations of remediation technologies but cannot capture the full extent of time-dependent reactions, leaving uncertainties in field-scale deployment. Here, long-term testing will be conducted on select technologies based on their performance in short-term testing. The overall objective is to directly address the challenges described above by generating and analyzing data on long-term (2–10 years) efficacy of selected remediation technologies, integrating this understanding into models, and providing critical input for remediation planning, monitoring, and 5-year review cycles for field-implemented remedies. Specific objectives include: 1. Evaluating long-term efficiency of promising remedies under site-specific conditions. 2. Generating robust parameters for modeling, reducing uncertainty in predictive simulations. 3. Advancing integrated monitoring by combining geochemical and geophysical observations. 4. Informing field-scale implementation by incrementally advancing technologies, identifying failure mechanisms early, and prioritizing robust, cost-effective remedies. Through systematic evaluation of technologies in laboratory-scale column experiments, integrated monitoring, and modeling support, this project is designed to bolster confidence in the long-term robustness of selected technologies. The ultimate outcome is the identification and deployment of more reliable, cost-effective remedies that safeguard human health and the environment while reducing the uncertainties that have historically hindered cleanup progress at Hanford Site. An experimental approach was developed and initiated for long-term testing potentially up to 10 years. The table below summarizes the experimental approach developed for testing select technologies and presented in this multi-year experimental test plan.
In compressible turbulence models, it is assumed that the Favre-mean velocities are suitable approximations to the Reynolds-mean velocities in order to close unknown terms. This neglects, in the mean momentum and energy equations, the contribution to the stress and work terms by the mean of the fluctuating Favre velocity, a quantity proportional to the turbulent mass flux. As the stress and work terms do not introduce any new unknown correlations requiring closure in either k-epsilon or Reynolds stress closures and because the exact form of the terms can, with little additional work, be carried there is no need to make any modeling assumptions. In the Reynolds stress equations the viscous terms appear naturally in Reynolds variables while the problem is posed in Favre variables. In the process of splitting the viscous terms into the viscous transport terms, carried in Favre variables, and the dissipation terms, carried in Reynolds variables, important contributions from the mass flux appear. The accurate accounting of these terms is important for any consistent near wall modeling and the retention of the mass flux terms is important in complex compressible turbulent flows.
High long-term soil moisture may either stimulate or inhibit soil organic carbon (SOC) losses through changes to mineral and chemical composition, and resultant organo-mineral interactions. Yet, the trade-off between mineralization and accrual of SOC under long-term variation in unsaturated soil moisture remains uncertain. We tested the underexplored relationships between long-term soil moisture and organo-mineral chemical composition and its implications for SOC persistence in an experimental field in New York, USA, with differences in long-term mean soil volumetric water content (0–0.15 m depth) ranging from 0.40 to 0.63 (v/v) during the growing season. Long-term soil moisture across 20 subplots on four fallow plots were positively correlated with SOC (R 2 = 0.23; P = 0.019, n = 20), mineral-associated organic matter (MAOM) content (g fraction/g soil) (R 2 = 0.44; P = 0.001; n = 20) and occluded particulate organic matter (oPOM) content (R 2 = 0.18; P = 0.033; n = 20). Higher long-term soil moisture was associated with a decrease in the relative content of sodium pyrophosphate extractable Fe (R 2 = 0.33; P < 0.005; n = 20), an increase in sodium dithionite extractable Fe (R 2 = 0.44; P < 0.001; n = 20), and an increase in SOC retention by non-crystalline Al pools (R 2 = 0.51; P = 0.0002 for sodium pyrophosphate extracts, R 2 = 0.41; P = 0.0014 for hydroxylamine hydrochloride extracts; n = 20 for both). Increasing long-term soil moisture was associated with a four-fold increase in microbial biomass C (per unit SOC) and lower metabolic quotient (R 2 = 0.56, P < 0.001). MAOM fractions of high-moisture soils had lower C:N (from C:N 9.5 to 9.0, R 2 = 0.27, P = 0.011, n = 20). Consistent with decreasing C:N, increasing decomposition with increasing moisture was reflected by a 15% and 10% greater proportion of oxidized carboxylic-C to aromatic-C and O-alkyl C, respectively, as measured with 13 C NMR, and a more pronounced FTIR signature of N-containing proteinaceous compounds in high-moisture MAOM fractions, indicative of microbial metabolites and transformation products. A partial least squares regression showed that SOC content increased with greater long-term moisture (P = 0.019), pyrophosphate-extractable Al (P = 0.0001), and exchangeable Ca (P = 0.013). Here our results show that higher long-term soil moisture resulted in SOC accrual by enhancing conversion of plant inputs into microbial biomass that interacts with reactive minerals.
In this work, we conducted a suite of experiments to evaluate the long-term permeability evolution of shale rocks under constant effective stress, before, during, and after interaction with supercritical carbon dioxide (scCO 2 ). To do so, we measured the time-dependent evolution of argon permeabilities for the pre- and post-reacted samples (i.e., before and after long-term interaction with scCO 2 ). In addition, we obtained permeability evolution during long-term interaction with scCO 2 . The samples showed either relatively constant permeabilities or a moderate decrease during pre reaction long-term argon tests. The permeability evolution during long-term CO 2 tests showed continuous increase, continuous decrease, or cycles of increase/decrease in permeability. The long term response of the samples to CO 2 included phenomena such as 20 (i) salt precipitation, (ii) swelling-induced cracks, and (iii) carbonate dissolution. While it is obvious that salt precipitation and swelling-induced cracks decrease and increase the permeability, respectively, the sample response to carbonate dissolution proved to be more complex and may increase or decrease the permeability. The permeability evolution during post-reaction long-term argon injection is also affected by the contribution of each of these three phenomena, during long-term interaction with CO 2 . We observe increase, decrease, and constant permeability evolution during post-reaction argon tests. Our experiments reveal that the initial permeability of the samples plays a significant role on the long-term permeability response of shales in the presence argon and scCO 2 fluids. This study shows, when shales are hydraulically fractured with CO 2 , their initial permeability has a more significant role than their permeability evolution over time.
Abstract Global soil organic carbon (SOC) stocks may decline with a warmer climate. However, model projections of changes in SOC due to climate warming depend on microbially-driven processes that are usually parameterized based on laboratory incubations. To assess how lab-scale incubation datasets inform model projections over decades, we optimized five microbially-relevant parameters in the Microbial-ENzyme Decomposition (MEND) model using 16 short-term glucose (6-day), 16 short-term cellulose (30-day) and 16 long-term cellulose (729-day) incubation datasets with soils from forests and grasslands across contrasting soil types. Our analysis identified consistently higher parameter estimates given the short-term versus long-term datasets. Implementing the short-term and long-term parameters, respectively, resulted in SOC loss (–8.2 ± 5.1% or –3.9 ± 2.8%), and minor SOC gain (1.8 ± 1.0%) in response to 5 °C warming, while only the latter is consistent with a meta-analysis of 149 field warming observations (1.6 ± 4.0%). Comparing multiple subsets of cellulose incubations (i.e., 6, 30, 90, 180, 360, 480 and 729-day) revealed comparable projections to the observed long-term SOC changes under warming only on 480- and 729-day. Integrating multi-year datasets of soil incubations (e.g., > 1.5 years) with microbial models can thus achieve more reasonable parameterization of key microbial processes and subsequently boost the accuracy and confidence of long-term SOC projections.
Abstract High latitude forests cope with considerable variation in moisture and temperature at multiple temporal scales. To assess how their photosynthetic physiology responds to short- and long-term temperature variation, we measured photosynthetic capacity for four tree species growing in an open-air experiment in the boreal-temperate ecotone `Boreal Forest Warming at an Ecotone in Danger' (B4WarmED). The experiment factorially manipulated temperature above- and below-ground (ambient, +3.2 °C) and summer rainfall (ambient, 40% removal). We measured A/Ci curves at 18, 25 and 32 °C for individuals of two boreal (Pinus banksiana Lamb., Betula papyrifera Marsh.) and two temperate species (Pinus strobus L., Acer rubrum L.) experiencing the long-term warming and/or reduced-rainfall conditions induced by our experimental treatments. We calculated the apparent photosynthetic capacity descriptors VCmax,Ci and Jmax,Ci and their ratio for each measurement temperate. We hypothesized that (i) VCmax,Ci and Jmax,Ci would be down-regulated in plants experiencing longer term (e.g., weeks to months) warming and reduced rainfall (i.e., have lower values at a given measurement temperature), as is sometimes found in the literature, and that (ii) plants growing at warmer temperatures or from warmer ranges would show greater sensitivity (steeper slope) to short-term (minutes to hours) temperature variation. Neither hypothesis was supported as a general trend across the four species, as there was not a significant main effect (across species) of either warming or rainfall reduction on VCmax,Ci and Jmax,Ci. All species markedly increased VCmax,Ci and Jmax,Ci (and decreased their ratio) with short-term increases in temperature (i.e., contrasting values at 18, 25 and 32 °C), and those responses were independent of long-term treatments and did not differ among species. The Jmax,Ci:VCmax,Ci ratio was, however, significantly lower across species in warmed and reduced rainfall treatments. Collectively, these results suggest that boreal trees possess considerable short-term plasticity that may allow homeostasis of VCmax,Ci and Jmax,Ci to a longer term temperature treatment. Our results also caution against extrapolating results obtained under controlled and markedly contrasting temperature treatments to responses of photosynthetic parameters to more modest temperature changes expected in the near-term with climate warming in field conditions.
Distributed wind projects, which are connected at the distribution level of an electricity system or in off-grid applications to serve specific or local energy needs, often rely solely on wind resource models to establish wind speed and energy generation expectations. Historically, anemometer loan programs have provided an affordable avenue for more accurate onsite wind resource assessment, and the lowering cost of lidar systems has shown similar advantages for more recent assessments. While a full 12 months of onsite wind measurement is the standard for correcting model-based long-term wind speed estimates for utility-scale wind farms, the time and capital investment involved in gathering onsite measurements must be reconciled with the energy needs and funding opportunities that drive expedient deployment of distributed wind projects. Much literature exists to quantify the performance of correcting long-term wind speed estimates with 1 or more years of observational data, but few studies explore the impacts of correcting with months-long observational periods. This study aims to answer the question of how short you can go in terms of the observational time period needed to make impactful improvements to model-based long-term wind speed estimates. Three algorithms, multivariable linear regression, adaptive regression splines, and regression trees, are evaluated for their skill at correcting long-term wind resource estimates from the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5) using months-long periods of observational data from 66 locations across the US. On average, correction with even 1 month of observations provides significant improvement over the baseline ERA5 wind speed estimates and produces median bias magnitudes and relative errors within 0.22 m s −1 and 4 percentage points of the median bias magnitudes and relative errors achieved using the standard 12 months of data for correction. However, in cases when the shortest observational periods (1 to 2 months) used for correction are not well correlated with the overlapping ERA5 reference, the resultant long-term wind speed errors are worse than those produced using ERA5 without correction. Summer months, which are characterized by weaker relative wind speeds and standard deviations for most of the evaluation sites, tend to produce the worst results for long-term correction using months-long observations. The three tested algorithms perform similarly for long-term wind speed bias; however, regression trees perform notably worse than multivariable linear regression and adaptive regression splines in terms of correlation when using 6 months or less of observational data for correction. Translating the analysis to wind energy, median relative errors in the capacity factor are on average within 10 % using 1 month of training. If the observation period used for correction is not well correlated with the reference data, however, misrepresentation of the observed capacity factor can be substantial. The risk associated with poor correlation between the observed and reference datasets decreases with increasing training period length. In the worst-correlation scenarios, the median capacity factor relative errors from using 1, 3, and 6 months are within 47 %, 26 %, and 16 %, respectively.
This report summarizes a risk informed, performance-based and technology-inclusive approach to determine source terms for dose related assessments at advanced nuclear facilities. This approach uses a graded process which allows both the non-mechanistic source terms calculation methods, which adopt conservative approaches and assumptions based on known physical and chemical principles, and more importantly the mechanistic source term calculation methods, which consider design-specific scenarios and use best estimate models with uncertainty quantification for a range of licensing basis events (LBEs), to be used for the design and licensing of advanced nuclear technologies. The source terms developed with this graded approach, and radionuclide inventories elsewhere in the facility that are determined during source term analysis, can be used to address licensing issues to support the application processes of 10 CFR Part 50 for a construction permit and operating license or 10 CFR Part 52 for a Combined Operating License (COL), Standard Design Certification, Early Site Permit, Standard Design Approval or Manufacturing License. They can also be used for other purposes including equipment environmental qualification, control room habitability analyses, and assessments of severe accident risks in environmental impact statements. There are many advanced reactor concepts being developed including high temperature gas-cooled reactor (HGTR), sodium-cooled fast reactor (SFR), lead-cooled fast reactor (LFR), molten salt reactor (MSR), micro-reactor, etc. The graded approach presented in this report for source terms determination is, to the extent possible, generic to any of these reactor designs and to future reactor designs. This report provides information on the review of the regulatory foundation for use of conservative bounding source terms as well as event-specific mechanistic source terms for advanced nuclear reactor designs.
The many-body expansion (MBE) was applied to homometallic and heterometallic trimers of metals with ns 1 , ns 2 , and ns 2 p 1 configurations to investigate its convergence, the magnitude and nature (stabilizing/destabilizing) of the individual terms and seek an understanding of their variation across the different families of clusters. In particular, we examined the series of alkali metals (Li 3 , Na 3 , K 3 , Li 2 Na, LiNa 2 ), alkali metal borides (Li 2 B and LiB 2 ), and alkaline earth metals (Be 3 , Be 2 Mg, BeMg 3 , and Mg 3 ) trimers, as well as sodium clusters Na n , n = 2–5. Here, we found that there is no uniform contribution (stabilizing or destabilizing) across the series in the different families of trimers. For instance, the 2-B term stabilizes the ground states of the Na 3 (doublet), Na 4 (singlet), and Na 5 (doublet) clusters, and the 3-B term destabilizes them; however, the opposite holds for the quartet state of the Li 3 , Li 2 Na, LiNa 2 , and Na 3 clusters (destabilizing 2-B, stabilizing 3-B). Substituting Li with B in the quartet state of Li 3 results in a significant reduction of the 3-B term amounting to 16% (Li 2 B) and 5% (LiB 3 ) of the binding energy. On the contrary, the ground states of the alkaline earth metal clusters (Be 3 , Be 2 Mg, BeMg 3 , and Mg 3 ) are stabilized by the 3-B term, while the 2-B term destabilizes them. Overall, we find that the 3-B terms significantly stabilize the high-spin multiplicity states of the ns 1 configurations and the low-spin states of the ns 2 configurations. Finally, as the size of the metal increases, the contribution of the 3-B term to the binding energy decreases due to the longer metal–metal bond distances.
Recently, recurrent deep networks have shown promise to harness newly available satellite–sensed data for long–term soil moisture projections. However, to be useful in forecasting, deep networks must also provide uncertainty estimates. Here we evaluated Monte Carlo dropout with an input–dependent data noise term (MCD+N), an efficient uncertainty estimation framework originally developed in computer vision, for hydrologic time series predictions. MCD+N simultaneously estimates a heteroscedastic input–dependent data noise term (a trained error model attributable to observational noise) and a network weight uncertainty term (attributable to insufficiently constrained model parameters). Although MCD+N has appealing features, many heuristic approximations were employed during its derivation, and rigorous evaluations and evidence of its asserted capability to detect dissimilarity were lacking. To address this, we provided an in–depth evaluation of the scheme's potential and limitations. We showed that for reproducing soil moisture dynamics recorded by the Soil Moisture Active Passive (SMAP) mission, MCD+N indeed gave a good estimate of predictive error, provided that we tuned a hyperparameter and used a representative training data set. The input–dependent term responded strongly to observational noise, while the model term clearly acted as a detector for physiographic dissimilarity from the training data, behaving as intended. However, when the training and test data were characteristically different, the input–dependent term could be misled, undermining its reliability. Additionally, due to the data–driven nature of the model, data noise also influences network weight uncertainty, and therefore the two uncertainty terms are correlated. Altogether, this approach has promise, but care is needed to interpret the results.