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To develop an innovative yet practically implementable mitigation technique for the most probable impact threat of an asteroid or comet with short warning time (i.e., when we don't have sufficient warning times for a deflection mission).
The NASA Global Modeling and Assimilation Office (GMAO) Sub-seasonal to Seasonal (S2S) prediction system is being readied for a major upgrade. An important factor in successful extended range forecasting is the definition of the ensemble. Our overall strategy is to run a relatively large ensemble of about 40 members up to 3 months (focusing on the sub-seasonal forecast problem), after which we sub-sample the ensemble, and continue the forecast with about 10 members (up to 12 months). Here we present the results of our testing of various ways to generate the initial perturbations and the validation of a stratified sampling approach for choosing the members of the smaller ensemble. For the initialization of the ensemble we propose a combination of lagged and burst initial conditions. To generate perturbations for the burst ensemble members we used scaled differences of pairs of analysis states (chosen randomly from the corresponding season) separated by 1-10 days. We consider perturbing separately the atmosphere and the ocean, or both. By varying the separation times between the analysis states, we are able to produce perturbations that resemble well-known modes of variability. Focusing on the ENSO SST indices, we found that all types of perturbations are important for the ensemble spread with, however, considerable differences in the timing of the impacts on spread for the atmospheric and oceanic perturbations.Our initial (larger) ensemble size was determined so as to maximize the skill of predicting some of the leading modes of boreal winter atmospheric modes (namely the NAO, PNA and AO). Since it is not feasible for us to run with the larger ensemble beyond about 3 months, we employ a stratified sampling procedure that identifies the emerging directions of error growth to subset the ensemble. By comparing the results from the stratified ensemble with that of the randomly sampled ensemble of the same size, we find that the former provides substantially better estimates the mean of the original large ensemble.
GMAO Sub/Seasonal prediction system (S2S) is being readied for a major upgrade to GEOS-S2S Version 3. An important factor in successful extended range forecast is the definition of an ensemble For initialization of the ensemble we propose a combination of lagged and burst initial conditions. We plan to run a relatively large ensemble of 40 members for sub-seasonal forecast (up to 3 months), at which point we sub-sample the ensemble, and continue the forecast with 10 members (up to 12 months). Here we present the results of the extensive testing of various ways to generate the perturbations to the initial conditions and the validation of the stratified sampling strategy we chose.To generate perturbations for the burst ensemble members we used scaled differences of pairs of analysis states separated by 1-10 days, randomly chosen from a corresponding season. We considered perturbing separately only the atmospheric fields or only the ocean or both of the forecast initial conditions. Considering varying separation times between the analysis states, we were able to produce perturbations sampling various modes of variability. Focusing on the ENSO SST indices, we found that all types of perturbations are important for the ensemble spread.Our ensemble size for sub-seasonal forecasts was determined as to maximize the skill of predicting some of the leading modes of boreal winter atmospheric modes, NAO, PNA and AO. It is not feasible to run equally large ensemble for seasonal forecasts. Using a stratified sampling procedure we can identify the emerging directions of error growth. By comparing the stratified ensemble with randomly sampled ensemble of the same size, we were able to show that the former better estimates the mean of the original large ensemble.
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Abstract not provided.
Power optimal single axis articulating PV array motion for Space Station Freedom is investigated. The motivation is to eliminate one of the articular joints to reduce Station costs. Optimal (maximum power) Beta tracking is addressed for local vertical local horizontal (LVLH) and non-LVLH attitudes. Effects of intra-array shadowing are also presented. Maximum power availability while Beta tracking is compared to full sun tracking and optimal alpha tracking. The results are quantified in orbital and yearly minimum, maximum, and average values of power availability.
This paper reports results of an investigation into developing a single failure tolerant pyrotechnic linear separation system which features completely redundant explosive trains suitable for human spaceflight. It is a follow up to “Flat-H Redundant Frangible Joint Design Evolution 2017” and “Flat-H Redundant Frangible Joint Evolution”. The paper chronicles the history of the redundant frangible joint development program including testing, analysis, and design improvements from 2014 to the present culminating in a successful proof-of-concept prototype. The paper describes work done to address debris control and containment of combustion products. A performance optimization strategy is presented along with optimization results. Additionally a novel containment manifold design is presented with test results.
The data were developed to evaluate the capacity of existing and potential new surface water supply infrastructure to meet projected public water demands across districts in Texas under multiple future socioeconomic and climate scenarios. The database integrates hydrologic, water quality, infrastructure, energy, cost, demographic, and demand-projection information for candidate surface water supply locations. Candidate sites include stream reaches, waterbodies, reservoir surplus locations, and potential new reservoir sites. Water availability is characterized using historical and projected flow conditions, while site suitability is evaluated using five indicators: Water Availability Index (WAI), Water Quality Index (WQI), Energy Requirement Index (ERI), Water Treatment Cost (WTC), and Water Infrastructure Cost (WIC). The datasets include statewide candidate-site information, district-level demand projections under Shared Socioeconomic Pathways (SSPs), runoff-based allocation constraints, climate-stress metrics, and optimization outputs evaluating alternative infrastructure planning strategies. Optimization results compare Business-as-Usual (BAU) and All Surface Water (AllSW) demand-management approaches under both scaled and fixed cost-cap strategies. Associated validation datasets provide district-level feasibility assessments, infrastructure selection outcomes, cost-cap utilization, demand satisfaction metrics, and constraint diagnostics. Additional datasets quantify projected changes in storage and flow conditions as well as water availability stress for both existing and newly selected intake locations under the SSP5 scenario for mid-century and late-century climate conditions. Together, these datasets support assessment of the extent to which surface-water infrastructure expansion and diversification strategies can satisfy future public water demands while accounting for hydrologic, economic, and planning constraints across Texas. Dataset(s) Description Dataset_preoptimization.xlsx Comprehensive pre-optimization dataset containing candidate water-supply sites and associated hydrologic, water-quality, infrastructure, climate, demographic, runoff, and demand-projection variables used as inputs to the optimization analyses. Includes variable descriptions and the full statewide candidate-site database. District_level_site_selection.zip - Compressed archive containing all SSP-specific district-level optimization result files MESIO_ssp1_results.xlsx District-level site selection results for SSP1 (MESIO). Includes variable descriptions, BAU and AllSW site-selection results under scaled and fixed cost strategies, and district-level validation diagnostics. MESID_ssp2_results.xlsx District-level site selection results for SSP2 (MESID). Includes variable descriptions, BAU and AllSW site-selection results under scaled and fixed cost strategies, and district-level validation diagnostics. LCMRD_ssp3_results.xlsx District-level site selection results for SSP3 (LCMRD). Includes variable descriptions, BAU and AllSW site-selection results under scaled and fixed cost strategies, and district-level validation diagnostics. IRDev-Low_ssp4l_results.xlsx District-level site selection results for SSP4-Low (IRDev-Low). Includes variable descriptions, BAU and AllSW site-selection results under scaled and fixed cost strategies, and district-level validation diagnostics. IRDev-High_ssp4h_results.xlsx District-level site selection results for SSP4-High (IRDev-High). Includes variable descriptions, BAU and AllSW site-selection results under scaled and fixed cost strategies, and district-level validation diagnostics. RSIM_ssp5_results.xlsx District-level site selection results for SSP5 (RSIM). Includes variable descriptions, BAU and AllSW site-selection results under scaled and fixed cost strategies, and district-level validation diagnostics. tx_hydrological_stress.xlsx Hydrological stress dataset for existing and newly selected intake locations. Includes projected mid-century and late-century changes, gain/loss classifications, planning strategy information, and accompanying variable descriptions. Also includes water-stress metrics derived from historical and projected low-flow conditions.
Reactor designs with mixed catalysts play an important role in transforming a multiple reactor system to single-shot reactors. In addition to savings in capital and ease of implementation, single-shot reactors are useful to break equilibrium limitations, thereby increasing the yield and selectivity of desired product as shown in previous studies. However, the nonlinear and highly exothermic nature of mixed-catalyst systems makes it difficult for commercial process simulation and optimization tools to optimize these systems. This study describes the development and application of optimization strategies for mixed-catalyst, single-shot reactors for syngas to olefin (STO) processes. Finding the optimal catalyst distribution is challenging and requires advanced solution strategies for singular optimal control problems, which are poorly conditioned and often lead to flat response surfaces. The graded bed and partial-moving finite-element approaches are used to find the optimal catalyst distribution that maximizes the olefins yield. A 1.3% increase in the yield is observed from one zone to three zones. The yield further improves from three zones to the exact infinite dimensional solution by 0.2%. This improvement can be realized in practice by changing only the catalyst distribution, without any extra investment. Lastly, the results suggest that a mixed-catalyst single shot reactor bed can be applied to other reaction mechanisms to increase reactor performance.
Atomic layer deposition (ALD) is a highly controllable thin film synthesis approach with applications in computing, energy, and separations. The flexibility of ALD means that it can access a massive chemical catalogue; however, this chemical and process diversity results in significant challenges in determining processing parameters that result in stable and uniform film growth with minimal precursor consumption. In situ measurements of the ALD growth per cycle (GPC) can accelerate process development but it still requires expert intuition and time-consuming trial and error to identify acceptable processing parameters. This procedure is made more difficult by the presence of experimental noise in the GPC values and the complexity of ALD surface chemistries. Here, a need exists for efficient optimization approaches capable of autonomously determining processing conditions resulting in optimal ALD film growth. In this work, we present the development of three optimization strategies and compare their performance in optimizing four simulated ALD processes. Furthermore, the effect of noise in the GPC measurements on optimization convergence is studied.
Trapped ions (TIs) are at the forefront of quantum computing implementation, offering unparalleled coherence, fidelity, and connectivity. However, the scalability of TI systems is hampered by the limited capacity of individual ion traps, necessitating intricate ion shuttling for advanced computational tasks. The quantum charge-coupled device (QCCD) framework has emerged as a promising solution, facilitating ion mobility for universal quantum computation. Current QCCD architectures predominantly feature a linear topology, which is increasingly recognized as inefficient for complex quantum operations. Anticipating the shift toward more efficacious designs, this article introduces an innovative quantum scheduling strategy optimized for parallel QCCD topologies. Our strategy proposes a probabilistic formula for ion movement, alongside ingenious methods for local layer generation and layer compression, yielding a significant reduction in ion shuttle times. Through simulations, we demonstrate that our strategy not only substantially outstrips the linear model but also exhibits better performance over other parallel strategies that employ greedy algorithms. This is achieved through our nuanced resolution of complexities, such as traffic blocks and trap capacity limitations. The consequent reduction in shuttle operations leads to lower energy consumption and an enhancement in the quantum computer's fidelity, ultimately accelerating program execution times.
A new general-purpose optimization program for engineering design is described. ADS (Automated Design Synthesis) is a FORTRAN program for nonlinear constrained (or unconstrained) function minimization. The optimization process is segmented into three levels: Strategy, Optimizer, and One-dimensional search. At each level, several options are available so that a total of nearly 100 possible combinations can be created. An example of available combinations is the Augmented Lagrange Multiplier method, using the BFGS variable metric unconstrained minimization together with polynomial interpolation for the one-dimensional search.
A new general-purpose optimization program for engineering design is described. ADS (Automated Design Synthesis - Version 1.10) is a FORTRAN program for solution of nonlinear constrained optimization problems. The program is segmented into three levels: strategy, optimizer, and one-dimensional search. At each level, several options are available so that a total of over 100 possible combinations can be created. Examples of available strategies are sequential unconstrained minimization, the Augmented Lagrange Multiplier method, and Sequential Linear Programming. Available optimizers include variable metric methods and the Method of Feasible Directions as examples, and one-dimensional search options include polynomial interpolation and the Golden Section method as examples. Emphasis is placed on ease of use of the program. All information is transferred via a single parameter list. Default values are provided for all internal program parameters such as convergence criteria, and the user is given a simple means to over-ride these, if desired.
NASA's Gateway plans to operate in an L2 9:2 lunar synodic Near Rectilinear Halo Orbit (NRHO) for nominal operations. During the mission's lifetime, scenarios may arise where rephasing within the NRHO or loitering in an alternate trajectory are advantageous. This investigation discusses two strategies to perform this rephasing in the NRHO over a relatively short timespan. The first method uses two maneuvers to shift the phase along a single transfer arc. The second strategy employs four maneuvers, leveraging an intermediate orbit for rephasing. An optimization strategy is formulated to construct locally optimal solutions in the Circular Restricted Three-Body Problem (CR3BP) and verified in a higher-fidelity ephemeris model.
Microbial enzyme-mediated soil organic matter (SOM) decomposition regulates many key ecosystem functions, such as elemental cycling, soil carbon sequestration, and soil fertility. However, representing microbial processes in Earth system models (ESMs) remains challenging due to a limited understanding of the spatial patterns of diverse microbial functions responsible for soil carbon (C), nitrogen (N), and phosphorus (P) cycling as well as the underlying mechanisms regulating their relative abundances across various environments. We collected published metagenomics data across the continental US (CONUS) to identify hundreds of microbial genes involved in soil C, N, and P cycling and grouped them into eight enzyme functional classes (EFCs). Each EFC represented a group of gene-encoded potential enzymes that decompose similar soil compounds. By integrating the abundances of omics-informed EFCs with the corresponding environmental information, we trained a machine learning (ML) model to identify key edaphic, climate, and vegetation factors regulating the abundances of each EFC. Quantitative analysis of effects of these factors revealed that the spatial distribution of eight EFCs for soil C, N, and P cycling across CONUS reflected potential resource optimization strategies of microbial communities under nutrient limitation, preferential organic-mineral associations, and climatological stresses. This insight, together with the interpreted ML tool and the CONUS-level benchmark for EFCs abundances, paves the way for parameterizing environmental-regulated microbial functional dynamics in biogeochemical models.