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Results for “Constraint Reduction”

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.

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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Nonlinear Interface Reduction for Time-Domain Analysis of Hurty/Craig-Bampton Superelements with Frictional Contact

Virtual prototyping in engineering design relies today on modern numerical models of contacting structures with accurate resolution of interface mechanics, which strongly affect the system-level stiffness and energy dissipation due to frictional losses. High-fidelity modeling within the localized interfaces is required to resolve local quantities of interest that may drive design decisions. The high-resolution finite element meshes necessary to resolve inter-component stresses tend to be computationally expensive, particularly when the analyst is interested in response time histories. The Hurty/Craig-Bampton (HCB) transformation is a widely used method in structural dynamics for reducing the interior portion of a finite element model while having the ability to retain all nonlinear contact degrees of freedom (DOF) in physical coordinates. These models may still require many DOF to adequately resolve the kinematics of the interface, leading to inadequate reduction and computational savings. This study proposes a novel interface reduction method to overcome these challenges by means of system-level characteristic constraint (SCC) modes and properly orthogonal interface modal derivatives (POIMDs) for transient dynamic analyses. Both SCC modes and POIMDs are computed using the reduced HCB mass and stiffness matrices, which can be directly computed from many commercial finite element analysis software. Comparison of time history responses to an impulse-type load in a mechanical beam assembly indicate that the interface-reduced model correlates well with the HCB truth model. Localized features like slip and contact area are well-represented in the time domain when the beam assembly is loaded with a broadband excitation. The proposed method also yields reduced-order models with greater critical timestep lengths for explicit integration schemes.

42 ENGINEERING↗
NASA NTRS1977

The development of a six degree-of-constraint robot performance evaluation test

A remote manipulator performance evaluation test was developed to test certain tool mating configurations not possible with the standard peg-in-hole type of test. The test attempted to evaluate robot manipulator performance over a full range of six degrees of freedom of motion between a tool and its intended receptacle. The test consists primarily of four different tool geometries and three different receptacle geometries which provide for a progressive reduction in the degrees of freedom of motion, and a progressive increase in the degrees of constraint (DOC) over motion, between the tool and the receptacle. The manipulation times of actual tools and couplings would be predicted by the times for the test tool most like it geometrically. In addition, the influence of four different transmission delays was tested. The results indicate that tool manipulation time can vary by a factor of about four depending on the degrees of constraint over final tool positioning.

Thompson, D. A.↗
NASA NTRS2019

Using Graph Coloring to Compute Total Derivatives More Efficiently in OpenMDAO

When they are applicable, gradient based optimization algorithms are the most efficient way to solve design optimization problems. Although gradient based methods are generally efficient, they can be made significantly more so through the usage of analytic techniques to compute the necessary total derivatives. The traditional forward (direct) and reverse (adjoint) analytic techniques have computational costs that scale linearly with the number of design variables and the number of constraints, respectively. In this work, we present an application of a graph coloring algorithm to the analytic techniques for computing total derivative Jacobians in order to achieve much better computational scaling than the pure analytic methods can provide alone. A detailed theoretical explanation of how coloring algorithms interact with analytic derivative methods is presented that illustrates specific types of sparsity patterns that must be present in total derivative Jacobians in order for this coloring technique to be effective. The new technique has been implemented as a feature in the OpenMDAO framework and the implementation is demonstrated on two example problems. The performance on the example problems up to 50% reduction in compute cost for optimizations with bi-directional coloring compared to traditional constraint aggregation. Additionally, the results show how coloring technique alleviates some of the numerical difficulties that constraint aggregation can cause, leading to the ability to solve larger problems. It is expected that the new method will have wide applicability to multidisciplinary optimization problems, and that its availability in OpenMDAO will offer significant computational savings for users without the need for them to implement the coloring algorithm themselves.

Trajectory↗
DOE OSTI2025

Variational autoencoders for at-source data reduction and anomaly detection in high energy particle detectors

Detectors in next-generation high-energy physics experiments face several daunting requirements, such as high data rates, damaging radiation exposure, and stringent constraints on power, space, and latency. To address these challenges, machine learning in readout electronics can be leveraged for smart detector designs, enabling intelligent inference and data reduction at-source. Variational autoencoders (VAEs) offer a variety of benefits for front-end readout; an on-sensor encoder can perform efficient lossy data compression while simultaneously providing a latent space representation that can be used for anomaly detection. Results are presented from low-latency and resource-efficient VAEs for front-end data processing in a futuristic silicon pixel detector. Encoder-based data compression is found to preserve good performance of off-detector analysis while significantly reducing the off-detector data rate as compared to a similarly sized data filtering approach. Furthermore, the latent space information is found to be a useful discriminator in the context of real-time sensor defect monitoring. Together, these results highlight the multifaceted utility of autoencoder-based front-end readout schemes and motivate their consideration in future detector designs.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

AMM: Adaptive Multilinear Meshes

Adaptive representations are increasingly indispensable for reducing the in-memory and on-disk footprints of large-scale data. Usual solutions are designed broadly along two themes: reducing data precision, e.g., through compression, or adapting data resolution, e.g., using spatial hierarchies. Additionally, recent research suggests that combining the two approaches, i.e., adapting both resolution and precision simultaneously, can offer significant gains over using them individually. However, there currently exist no practical solutions to creating and evaluating such representations at scale. In this work, we present a new resolution-precision-adaptive representation to support hybrid data reduction schemes and offer an interface to existing tools and algorithms. Through novelties in spatial hierarchy, our representation, Adaptive Multilinear Meshes (AMM), provides considerable reduction in the mesh size. AMM creates a piecewise multilinear representation of uniformly sampled scalar data and can selectively relax or enforce constraints on conformity, continuity, and coverage, delivering a flexible adaptive representation. AMM also supports representing the function using mixed-precision values to further the achievable gains in data reduction. We describe a practical approach to creating AMM incrementally using arbitrary orderings of data and demonstrate AMM on six types of resolution and precision datastreams. By interfacing with state-of-the-art rendering tools through VTK, we demonstrate the practical and computational advantages of our representation for visualization techniques. With an open-source release of our tool to create AMM, we make such evaluation of data reduction accessible to the community, which we hope will foster new opportunities and future data reduction schemes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

A feasibility study of dynamic adaptive radiotherapy for nonsmall cell lung cancer

Purpose: The final state of the tumor at the end of a radiotherapy course is dependent on the doses given in each fraction during the treatment course. This study investigates the feasibility of using dynamic adaptive radiotherapy (DART) in treating lung cancers assuming CBCT is available to observe midtreatment tumor states. DART adapts treatment plans using a dynamic programming technique to consider the expected changes of the tumor in the optimization process. Methods: DART is constructed using a stochastic control formalism framework. It minimizes the total expected number of tumor cells at the end of a treatment course, which is equivalent to maximizing tumor control probability, subject to the uncertainty inherent in the tumor response. This formulation allows for nonstationary dose distributions as well as nonstationary fractional doses as needed to achieve a series of optimal plans that are conformal to the tumor over time, i.e., spatiotemporally optimal plans. Sixteen phantom cases with various sizes and locations of tumors and organs-at-risk (OAR) were generated using in-house software. Each case was planned with DART and conventional IMRT prescribing 60 Gy in 30 fractions. The observations of the change in the tumor volume over a treatment course were simulated using a two-level cell population model. Monte Carlo simulations of the treatment course for each case were run to account for uncertainty in the tumor response. The same OAR dose constraints were applied for both methods. The frequency of replanning was varied between 1, 2, 5 (weekly), and 29 times (daily). The final average tumor dose and OAR doses have been compared to quantify the potential dosimetric benefits of DART. Results: The average tumor max, min, mean, and D95 doses using DART relative to these using conventional IMRT were 124.0%–125.2%, 102.1%–114.7%, 113.7%–123.4%, and 102.0%–115.9% (range dependent on the frequency of replanning). The average relative maximum doses for the cord and esophagus, mean doses for the heart and lungs, and D05 for the unspecified tissue resulting 84%–102.4%, 99.8%–106.9%, 66.9%–85.6%, 58.2%–78.8%, and 85.2%–94.0%, respectively. Conclusions: It is feasible to apply DART to the treatment of NSCLC using CBCT to observe the midtreatment tumor state. Potential increases in the tumor dose and reductions in the OAR dose, particularly for parallel OARs with mean or dose–volume constraints, could be achieved using DART compared to nonadaptive IMRT.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2005

Water Injection Feasibility for Boeing 747 Aircraft

Can water injection be offered at a reasonable cost to large airplane operators to reduce takeoff NO( sub x) emissions? This study suggests it may be possible. This report is a contract deliverable to NASA Glenn Research Center from the prime contractor, The Boeing Commercial Airplane Company of Seattle, WA. This study was supported by a separate contract to the Pratt & Whitney Engine Company of Hartford, CT (contract number NNC04QB58P). Aviation continues to grow and with it, environmental pressures are increasing for airports that service commercial airplanes. The feasibility and performance of an emissions-reducing technology, water injection, was studied for a large commercial airplane (e.g., Boeing 747 with PW4062 engine). The primary use of the water-injection system would be to lower NOx emissions while an important secondary benefit might be to improve engine turbine life. A tradeoff exists between engine fuel efficiency and NOx emissions. As engines improve fuel efficiency, by increasing the overall pressure ratio of the engine s compressor, the resulting increased gas temperature usually results in higher NOx emissions. Low-NO(sub x) combustors have been developed for new airplanes to control the increases in NO(sub x) emissions associated with higher efficiency, higher pressure ratio engines. However, achieving a significant reduction of NO(sub x) emissions at airports has been challenging. Using water injection during takeoff has the potential to cut engine NO(sub x) emissions some 80 percent. This may eliminate operating limitations for airplanes flying into airports with emission constraints. This study suggests an important finding of being able to offer large commercial airplane owners an emission-reduction technology that may also save on operating costs.

Daggett, David L.↗
DOE OSTI2013

Microbial Characterization of Halite and Groundwater Samples from the WIPP

This report summarizes the progress made in characterizing the microbial community structures within the WIPP repository, and in surrounding groundwaters. Through cultivation and DNA-based identification, the potential activity of these organisms is being inferred, thus leading to a better understanding of their impact on WIPP performance. WIPP halite constitutes the near-field microbial environment. Microbial activity in this setting is predicted by WIPP performance assessment (PA) to proceed from aerobic respiration, through nitrate reduction, to focus on sulfate reduction. The role of methanogenesis in the WIPP remains unclear, due to both energetic constraints imposed by a high-salt environment and substrate selectivity, so this role is no longer considered in PA. Members of the three biological domains — Bacteria, Archaea, and Eukarya (in this case, Fungi) — have been found associated with WIPP halite. Thus far, their activity has been limited to aerobic respiration. Archaea identified in WIPP halite thus far fall exclusively within the family Halobacteriaceae. These include Halobacterium noricense, a Halorubrum-like species, and a Halopenitus-like species. Bacterial signatures associated with WIPP halite include members of the phylum Proteobacteria — Halomonas, Pelomonas, Limnobacter, and Chromohalobacter — but only the latter has been isolated. Also detected and cultivated were Salinicoccus and Nesterenkonia spp. Three fungal species were isolated from halite (Cladosporium, Phoma, and Engyodontium). Although these were most likely introduced into the WIPP as contaminants from above-ground, their survival and potential role in the WIPP (e.g., cellulose degradation) is under investigation. WIPP groundwaters comprise the far-field microbial environment. Bacteria cultivated and identified from the overlying Culebra and nearby borehole groundwater are capable of aerobic respiration, denitrification, fermentation, metal reduction, and sulfate reduction, and are distributed across many different phyla. Their structural and metabolic diversity is dependent upon the ionic strength of the sampled groundwater, with a decrease in both at higher strength. Two of the Bacteria found in groundwater were also found in WIPP halite (Chromohalobacter sp. and Virgibacillus sp.). Archaea identified in groundwater include Halococcus saccharolyticus, Haloferax sp., and Natrinema sp. The differences in the microbial communities detected thus far in halite and groundwater suggest that there will be significant differences in the associated metabolic potential of the near- and far-field environments. Whereas the near-field is dominated by Archaea with more limited metabolic capabilities, the far-field is dominated by Bacteria with extremely broad capabilities. The authors wish to thank Russ Patterson and Dan Ferguson (DOE-CBFO) for their support, and Washington TRU Solutions for access to sampling. All research was performed by the Los Alamos National Laboratory Actinide Chemistry & Repository Science team at the Carlsbad Environmental and Monitoring Research Center, managed by New Mexico State University (NMSU) and located in Carlsbad, NM. Funding for this research is through the Waste Isolation Pilot Plant project, managed by the Department of Energy—Carlsbad Field Office.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Conjunctive management of surface and groundwater resources under projected future climate change scenarios

Our study introduces a mixed integer linear fractional programming (MILFP) method to optimize conjunctive use of future surface water and groundwater resources under projected climate change scenarios. The conjunctive management model maximizes the ratio of groundwater usage to reservoir water usage. Future inflows to the reservoirs were estimated from the future runoffs projected through hydroclimate modeling considering the Variable Infiltration Capacity model, and 11 sets of downscaled Coupled Model Intercomparison Project phase 5 global climate model projections. Bayesian model averaging was adopted to quantify uncertainty in future runoff projections and reservoir inflow projections due to uncertain future climate projections. Optimized conjunctive management solutions were investigated for a water supply network in northern Louisiana which includes the Sparta aquifer. Runoff projections under climate change scenarios indicate that runoff will likely decrease in winter and increase in other seasons. Ultimately, results from the developed conjunctive management model with MILFP indicate that the future reservoir water, even at 2.5% low inflow cumulative probability level, could counterbalance groundwater pumping reduction to satisfy demands while improving the Sparta aquifer through conditional groundwater head constraint.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The role of lattice dynamics in ferroelectric switching

Reducing the switching energy of ferroelectric thin films remains an important goal in the pursuit of ultralow-power ferroelectric memory and logic devices. Here, we elucidate the fundamental role of lattice dynamics in ferroelectric switching by studying both freestanding bismuth ferrite (BiFeO 3 ) membranes and films clamped to a substrate. We observe a distinct evolution of the ferroelectric domain pattern, from striped, 71° ferroelastic domains (spacing of ~100 nm) in clamped BiFeO 3 films, to large (10’s of micrometers) 180° domains in freestanding films. By removing the constraints imposed by mechanical clamping from the substrate, we can realize a ~40% reduction of the switching voltage and a consequent ~60% improvement in the switching speed. Our findings highlight the importance of a dynamic clamping process occurring during switching, which impacts strain, ferroelectric, and ferrodistortive order parameters and plays a critical role in setting the energetics and dynamics of ferroelectric switching.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Understanding boundary effects in quantum state tomography – One qubit case

For classical and quantum estimation with finite data sets, the estimation error can deviate significantly from its asymptotic (large data set) behavior. In quantum state tomography, a major reason for this is the existence of a boundary in the parameter space imposed by constraints, such as the positive semidefiniteness of density matrices. Intuitively, we should be able to reduce the estimation error by using our knowledge of these constraints. This intuition is correct for maximumlikelihood estimators, but the size of the reduction has not been evaluated quantitatively. In this proceeding, we evaluate the improvement in one qubit state tomography by using mathematical tools in classical statistical estimation theory. In particular, we show that the effect of the reduction decreases exponentially with respect to the number of data sets when the true state is mixed, and it remains at arbitrarily large data set when the true state is pure.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Integrating vehicle trajectory planning and arterial traffic management to facilitate eco-approach and departure deployment

Eco-approach and departure (EAD) enable continuous vehicle motion in urban signalized corridors. Since such a motion can extend to the EAD vehicles’ followers, it makes EAD a promising technology to benefit the traffic flow where automated vehicles and conventional vehicles coexist. Most existing EAD studies envision an ideal setting that neglects real-world operational conditions such as lane changes, multi-movement intersection configuration, partially automated fleet, and/or limited traffic state awareness. This study aims to fill the gap by designing an EAD algorithm considering real-world traffic operation constraints. The proposed algorithm uses a model predictive controller to minimize vehicle speed reduction and variation based on the real-time traffic signal control plan and measured queues at the intersection. The required inputs are readily available at many modern intersections. We observed that the proposed controller’s performance might degrade because of lane-changing maneuvers and lead-left turn traffic signals. These observations motivated our development of a lane change management strategy and a signal control implementation strategy to facilitate the EAD implementation. The lane change management strategies separate the EAD operations and lane-changing maneuvers in time and space. The signal control implementation strategy applies lag-left turn signals to enable EAD operation for both the through and left-turn vehicles. Compared to the non-EAD case, our EAD approach produces 2.5% to 7.8% energy savings while keeping similar intersection mobility. Notably, this approach brings about 2.5% to 3.6% energy savings in a 2% CAV case. This result demonstrates the feasibility of deploying EAD at low connected automated vehicle penetration rates.

Arterial corridor management↗
DOE OSTI2025

Acceleration of Power System Dynamic Simulations Using a Deep Equilibrium Layer and Neural ODE Surrogate

The dominant paradigm for power system dynamic simulation is to build system-level simulations by combining physics-based models of individual components. The sheer size of the system along with the rapid integration of inverter-based resources exacerbates the computational burden of running time domain simulations. Here, in this paper, we propose a data-driven surrogate model based on implicit machine learningspecifically deep equilibrium layers and neural ordinary differential equationsto learn a reduced order model of a portion of the full underlying system. The data-driven surrogate achieves similar accuracy and reduction in simulation time compared to a physics-based surrogate, without the constraint of requiring detailed knowledge of the underlying dynamic models. This work also establishes key requirements needed to integrate the surrogate into existing simulation workflows; the proposed surrogate is initialized to a steady state operating point that matches the power flow solution by design.

Neural ordinary differential equations↗
NASA NTRS1993

An improved linear ion trap physics package

This article describes an improvement in the architecture of the physics package used in the Linear Ion Trap (LIT)-based frequency standard recently developed at JPL. This new design is based on the observation that ions can be moved along the axis of an LIT by applied dc voltages. The state selection and interrogation region can be separated from the more critical microwave resonance region where the multiplied local oscillator signal is compared with the stable atomic transition. This separation relaxes many of the design constraints of the present units. Improvements include increased frequency stability and a substantial reduction in size, mass, and cost of the final frequency standard.

Prestage, J. D.↗
DOE OSTI2025

Use of Rig Parameter Data in Bit Constraint Models for Improved Drilling Performance at The Geysers

Surface parameter measurements are routinely used during deep well construction to monitor and guide drilling conditions for improved performance and reduced costs. However, these measurements are of reduced value without a standard to aid in evaluation and decision making. A method is demonstrated whereby drill bit constraint models are used to interpret drilling response parameters. Drill rig parameter data for well GDC-36 at the Geysers Geothermal Field Power were acquired by Geysers Power Company and drilling contractor Kenai Drilling using Pason US DataHub and evaluated. Drilling parameters are evaluated using laboratory-validated rock reduction models for predicting the phenomenological response of drag bits (Detournay and Defourny, 1992) along with other model constraints in computational algorithms. The method is used to evaluate overall bit performance, monitor bit integrity, and detect the presence of drillstring vibrations and other conditions contributing to bit failure; comparisons are made to observations of bit wear and damage. The method will be applied in real-time to improve decision-making on subsequent wells and has applicability to development of advanced analytics on future geothermal wells using real-time electronic drilling recorder (EDR) data for improved performance and reduced drilling costs.

15 GEOTHERMAL ENERGY↗
NASA NTRS2014

Mass Reduction: The Weighty Challenge for Exploration Space Flight

Meeting nutritional and acceptability requirements is critical for the food system for an exploration class space mission. However, this must be achieved within the constraints of available resources such as water, crew time, stowage volume, launch mass and power availability.  Due to resource constraints, exploration class missions are not expected to have refrigerators or freezers for food storage, and current per person food mass must be reduced to improve mission feasibility.  The Packaged Food Mass Reduction Trade Study (Stoklosa, 2009) concluded that the mass of the current space food system can be effectively reduced by decreasing water content of certain foods and offering nutrient dense substitutes, such as meal replacement bars and beverages. Target nutrient ranges were established based on the nutritional content of the current breakfast and lunch meals in the ISS standard menu. A market survey of available commercial products produced no viable options for meal replacement bar or beverage products. New prototypes for both categories were formulated to meet target nutrient ranges. Samples of prototype products were packaged in high barrier packaging currently used for ISS and underwent an accelerated shelf life study at 31 degC and 41 degC (50% RH) for 24 weeks. Samples were assessed at the following time points: Initial, 6 weeks, 12 weeks, and 24 weeks. Testing at each time point included the following: color, texture, water activity, acceptability, and hexanal analysis (for food bars only). Proof of concept prototypes demonstrated that meal replacement food bars and beverages can deliver a comparable macronutrient profile while reducing the overall mass when compared to the ISS Standard Menu. Future work suggestions for meal replacement bars: Reformulation to include ingredients that reduce hardness and reduce browning to increase shelf life. Micronutrient analysis and potential fortification. Sensory evaluation studies including satiety tests and menu fatigue. Water Intake Analysis: The water in thermostabilized foods is considered as part of a crewmember's daily water intake. Extensive meal replacement would require further analyses to determine if additional water provisioning would be required per crewmember negating some of the mass savings.

Kloeris, Vickie L.↗
DOE OSTI2025

RLMolLM: Reinforcement Learning-Enhanced Language Model Framework for Inverse Molecular Design

Inverse molecular design faces significant challenges due to vast chemical space and complex property requirements. While language models show promise for molecular generation, they struggle with validity, multi-property optimization, and structural constraints. This work presents RLMolLM, a reinforcement learning framework combining Proximal Policy Optimization (PPO) with genetic algorithms to address these limitations. Our approach optimizes multiple user-specified properties including quantitative estimates of drug-likeness (QED), synthetic accessibility (SA), and ADMET (absorption, distribution, metabolism, excretion, and toxicity) endpoints without requiring complete model retraining, while maintaining capability for scaffold-constrained generation where specific substructures must be preserved. We outperform state-of-the-art methods for molecular optimization, achieving best QED scores across GDB13, Moses, and Zinc datasets with up to 31% improvement over previous methods while maintaining excellent validity, uniqueness, and novelty metrics. For simultaneous multi-property optimization, our framework achieves substantial improvements in ADMET properties including 4.5-fold reduction in hERG toxicity and enhanced Caco-2 permeability compared to Moses dataset. Under structural constraints, the framework significantly improves molecular validity while preserving scaffolds and effectively optimizing properties. In conclusion, this versatile solution advances pharmaceutical and materials molecular design through effective integration of reinforcement learning and genetic algorithms with multi-property optimization and scaffold preservation.

Genetic algorithms↗