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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Air Pollution Inputs to the Mojave Desert By Fusing Surface Mobile and Airborne in Situ and Airborne and Satellite Remote Sensing: A Case Study of Interbasin Transport With Numerical Model Validation

Deserts are fragile and highly sensitive ecosystems that increasingly are affected by upwind urban areas and industrial activities. The Los Angeles Basin (LAB) contributes to poor air quality in downwind deserts including the Mojave Desert. Few studies have investigated potential air pollution inputs to the Mojave, whose fragile ecosystem includes endangered plant and animal species. Data were collected on 19 August 2015 by a mobile air quality laboratory, AMOG (AutoMObile trace Gas) Surveyor, that observed inputs can arise from the LAB as well as the San Joaquin Valley (SJV), California. The campaign used a strong methane (CH4) plume as a tracer for the downwind fate of emissions from Bakersfield area petroleum production and also measured ozone (O3). Additional in situ concurrent airborne GHG and O3 data were collected by AJAX - Alpha Jet Atmospheric eXperiment. Both AMOG and AJAX measure winds. Mojave Desert air quality was very poor (visibility ~4 km). Based on the winds, an additional source was inferred beyond the LAB and SJV Basins. Numerical transport modeling and analysis of aerosol lidar data collected the same day by the Cloud Profiling LiDAR onboard the Earth Research-2 stratospheric airplane demonstrated that fires in Northern California were responsible, with prevailing winds transporting air southwards along the eastern Sierra Nevada Range (Bishop Valley) to the Mojave. Whereas the southern and eastern Mojave are impacted by SJV and LAB outflow, the north Mojave generally avoids these inputs. This study shows it can be affected by even distant wildfires, which likely will increase in occurrence and intensity from climate change. Thus, regulatory efforts to reduce air quality impacts on the endangered Mojave ecosystem must include wildfires and also account for the significant differences between different regions of the Mojave. Currently, there is a paucity of studies, highlighting the critical need for field research.

Ira Leifer↗
NASA NTRS2021

Modeling Interplanetary Expansion and Deformation of CMEs with ANTEATR-PARADE II: Sensitivity to Input Parameters

Space weather predictions related to coronal mass ejections (CMEs) requires understanding how a CME is initiated and how its properties change as it propagates. While some parameters can be measured relatively easily near the Sun, others are much harder to disentangle from projected coronagraph images. Most predictions have been limited to the arrival time of a CME and include little to no information about the CME's internal properties. ANTEATR-PARADE represents the most thorough description of the interplanetary evolution of CMEs in a highly computationally-efficient model. (Kay & Nieves-Chinchilla, 2020) presents the derivation of this model, where we have added an elliptical cross section to the original arrival time model ANTEATR and introduced internal magnetic forces that, combined with the drag, can alter the shape of the central axis and cross section. ANTEATR-PARADE results include the transit time of CMEs, as well as the shape and size, propagation and expansion velocities, density, and magnetic field properties upon impact. We determine the dependence of each output on each of the ANTEATR-PARADE input parameters. For a fast CME, we see that the transit time and propagation velocity depend most strongly on inputs that modify the drag force whereas the inputs affecting the magnetic forces determine the expansion of the CME. We extend to other CMEs and _nd that the sensitivities change with CME scale. Magnetic forces become more important for an average CME whereas the drag force becomes more important for an extreme CME.

Coronal mass ejections↗
NASA NTRSDate not supplied

PMSM Parameter Estimation using a Linear Unknown Input Interval Observer

In this paper, the problem of permanent magnet synchronous motor (PMSM) speed and unknown load torque estimation is addressed. For this purpose, a interval unknown input observer (UIO) for linear time-invariant (LTI) systems is used. First, the PMSM model is linearized in order to make it in a suitable form for the linear interval UIO. Then, the interval UIO is applied to allow the joint estimation of the motor speed and the unknown load torque disturbance. The main advantages of this approach is that it not only allows the joint state and unknown input estimation, but also to take the different uncertainty sources into account. Indeed, taking model and measurement uncertainty into account is crucial. Assuming that the measurement noise and disturbances are bounded, lower and upper bounds are first computed for the unmeasured state (motor speed) and then for the unknown input (load torque). The proposed approach and its limitations are demonstrated with the nonlinear PMSM model derived from its equivalent electrical circuit.

Elinirina I Robinson↗
NASA NTRSDate not supplied

Estimation of Permanent Magnet Synchronous Motor Parameters using a Linear Unknown Input Interval Observer

This paper develops and evaluate a technique to estimate the speed and unknown load torque of a permanent magnet synchronous motor (PMSM). An interval unknown input observer (UIO) for linear time-invariant (LTI) systems is designed and utilized to estimate the PMSM parameters. Interval UIOs are advantageous as they allow for the estimation of both states and unknown inputs, while accounting for varying uncertainty sources, such as model and measurement uncertainties inherent in dynamic systems and processes. First, the nonlinear PMSM model is linearized to transform it to a suitable form for application of the linear interval UIO. Then, the interval UIO is applied to jointly estimate the motor speed and the unknown load torque disturbance. Assuming that the measurement noise and disturbances are bounded, lower and upper bounds are first computed for the unmeasured state (motor speed) and then for the unknown input (load torque). The proposed approach and its limitations are demonstrated for the nonlinear PMSM model derived from its equivalent electrical circuit.

Elinirina I Robinson↗
DOE OSTI2019

Single instruction multiple data page table walk scheduling at input output memory management unit

A data processing system includes a memory and an input output memory management unit that is connected to the memory. The input output memory management unit is adapted to receive batches of address translation requests. The input output memory management unit has instructions that identify, from among the batches of address translation requests, a later batch having a lower number of memory access requests than an earlier batch, and selectively schedules access to a page table walker for each address translation request of a batch.

Basu, Arkaprava↗
DOE OSTI2021

Integrated ELM and divertor power flux control using RMPs with low input torque in EAST in support of the ITER research plan

Experiments have been carried out at the EAST tokamak to study ITER-relevant scenario integration issues, related to edge localized mode (ELM) control in H-mode plasmas by the application of three-dimensional (3D) resonant magnetic perturbations (RMPs), which have a large impact on the execution of the ITER research plan. The EAST experiments have successfully demonstrated ELM suppression at normalized torque inputs similar to ITER. The application of RMP fields with high toroidal mode number (n = 4) reduces the impact of ELM control on energy and particle confinement compared to those use lower n (n = 1, 2) RMPs. Injection of successive pellets is found to be effective in increasing the plasma density in ELM-suppressed H-modes and reducing the divertor power without triggering large ELMs at EAST. Access to high recycling and radiative divertor conditions while maintaining ELM suppression has been demonstrated in EAST by the use of gas fuelling and neon impurity seeding. Both approaches have been found to be effective in reducing power fluxes to the divertor strike points in near-separatrix lobes for both n = 2 and n = 4 RMPs. Furthermore, reduction of power fluxes in off-separatrix lobes is only effective for n = 4 RMP application, which is consistent with magnetic topology modelling (including plasma response) results showing a shallow penetration into the confined plasma region of field lines connected to these lobes compared to n = 2. The EAST results support the use of high n 3D fields for ELM suppression in ITER high Q DT scenarios since they provide optimum integration features regarding energy and particle confinement, pellet fuelling, radiative divertor operation while eliminating ELM transient power loads and being compatible with low torque input.

pellet fuelling↗
NASA NTRS2016

NDARC NASA Design and Analysis of Rotorcraft - Input, Appendix 4

The NDARC code performs design and analysis tasks. The design task involves sizing the rotorcraft to satisfy specified design conditions and missions. The analysis tasks can include off-design mission performance analysis, flight performance calculation for point operating conditions, and generation of subsystem or component performance maps. The principal tasks (sizing, mission analysis, flight performance analysis) are shown in the figure as boxes with heavy borders. Heavy arrows show control of subordinate tasks. The aircraft description consists of all the information, input and derived, that denes the aircraft. The aircraft consists of a set of components, including fuselage, rotors, wings, tails, and propulsion. This information can be the result of the sizing task; can come entirely from input, for a fixed model; or can come from the sizing task in a previous case or previous job. The aircraft description information is available to all tasks and all solutions. The sizing task determines the dimensions, power, and weight of a rotorcraft that can perform a specified set of design conditions and missions. The aircraft size is characterized by parameters such as design gross weight, weight empty, rotor radius, and engine power available. The relations between dimensions, power, and weight generally require an iterative solution. From the design flight conditions and missions, the task can determine the total engine power or the rotor radius (or both power and radius can be fixed), as well as the design gross weight, maximum takeoff weight, drive system torque limit, and fuel tank capacity. For each propulsion group, the engine power or the rotor radius can be sized. Missions are defined for the sizing task, and for the mission performance analysis. A mission consists of a number of mission segments, for which time, distance, and fuel burn are evaluated. For the sizing task, certain missions are designated to be used for design gross weight calculations; for transmission sizing; and for fuel tank sizing. The mission parameters include mission takeoff gross weight and useful load. For specified takeoff fuel weight with adjustable segments, the mission time or distance is adjusted so the fuel required for the mission equals the takeoff fuel weight. The mission iteration is on fuel weight or energy. Flight conditions are specified for the sizing task, and for the flight performance analysis. For the sizing task, certain flight conditions are designated to be used for design gross weight calculations; for transmission sizing; for maximum takeoff weight calculations; and for anti-torque or auxiliary thrust rotor sizing. The flight condition parameters include gross weight and useful load. For flight conditions and mission takeoff, the gross weight can be maximized, such that the power required equals the power available. A flight state is defined for each mission segment and each flight condition. The aircraft performance can be analyzed for the specified state, or a maximum effort performance can be identified. The maximum effort is specified in terms of a quantity such as best endurance or best range, and a variable such as speed, rate of climb, or altitude.

Rotorcraft Input↗
NASA NTRS2020

Aircraft System Identification from Multisine Inputs and Frequency Responses

The identification of aircraft flight dynamics is often performed using frequency responses, which are nonparametric models that quantify the steady-state magnitude and phase of a dynamic system response to sinusoidal inputs, as a function of frequency. Frequency responses are computed from measured input and output data, and then model parameters, such as stability and control derivatives, are estimated to best fit a parametric model to the empirical frequency response data. The utility of this approach is due to the familiarity of engineers with frequency responses, a number of theoretical and practical advantages under specific conditions, the availability of software packages, and many other reasons.

System identification↗
NASA NTRS2022

NDARC - NASA Design and Analysis of Rotorcraft: Input and Data Structures - Vol 2

The NDARC code performs design and analysis tasks. The design task involves sizing the rotorcraft to satisfy specified design conditions and missions. The analysis tasks can include off-design mission performance analysis, flight performance calculation for point operating conditions, and generation of subsystem or component performance maps. Figure 1-1 illustrates the tasks. The principal tasks (sizing, mission analysis, flight performance analysis) are shown in the figure as boxes with heavy borders. Heavy arrows show control of subordinate tasks. The aircraft description (figure 1-1) consists of all the information, input and derived, that defines the aircraft. The aircraft consists of a set of components, including fuselage, rotors, wings, tails, and propulsion. This information can be the result of the sizing task; can come entirely from input, for a fixed model; or can come from the sizing task in a previous case or previous job. The aircraft description information is available to all tasks and all solutions (indicated by light arrows). The sizing task determines the dimensions, power, and weight of a rotorcraft that can perform a specified set of design conditions and missions. The aircraft size is characterized by parameters such as design gross weight, weight empty, rotor radius, and engine power available. The relations between dimensions, power, and weight generally require an iterative solution. From the design flight conditions and missions, the task can determine the total engine power or the rotor radius (or both power and radius can be fixed), as well as the design gross weight, maximum takeoff weight, drive system torque limit, and fuel tank capacity. For each propulsion group, the engine power or the rotor radius can be sized. Missions are defined for the sizing task, and for the mission performance analysis. A mission consists of a number of mission segments, for which time, distance, and fuel burn are evaluated. For the sizing task, certain missions are designated to be used for design gross weight calculations; for transmission sizing; and for fuel tank sizing. The mission parameters include mission takeoff gross weight and useful load. For specified takeoff fuel weight with adjustable segments, the mission time or distance is adjusted so the fuel required for the mission (burned plus reserve) equals the takeoff fuel weight. The mission iteration is on fuel weight or energy. Flight conditions are specified for the sizing task, and for the flight performance analysis. For the sizing task, certain flight conditions are designated to be used for design gross weight calculations; for transmission sizing; for maximum takeoff weight calculations; and for antitorque or auxiliary thrust rotor sizing. The flight condition parameters include gross weight and useful load. For flight conditions and mission takeoff, the gross weight can be maximized, such that the power required equals the power available. A flight state is defined for each mission segment and each flight condition. The aircraft performance can be analyzed for the specified state, or a maximum effort performance can be identified. The maximum effort is specified in terms of a quantity such as best endurance or best range, and a variable such as speed, rate of climb, or altitude. The aircraft must be trimmed, by solving for the controls and motion that produce equilibrium in the specified flight state. Different trim solution definitions are required for various flight states. Evaluating the rotor hub forces may require solution of the blade flap equations of motion.

NDARC↗
NASA NTRS2022

NDARC - NASA Design and Analysis of Rotorcraft: Input - Vol 3

The NDARC code performs design and analysis tasks. The design task involves sizing the rotorcraft to satisfy specified design conditions and missions. The analysis tasks can include off-design mission performance analysis, flight performance calculation for point operating conditions, and generation of subsystem or component performance maps. Figure 1-1 illustrates the tasks. The principal tasks (sizing, mission analysis, flight performance analysis) are shown in the figure as boxes with heavy borders. Heavy arrows show control of subordinate tasks. The aircraft description (figure 1-1) consists of all the information, input and derived, that defines the aircraft. The aircraft consists of a set of components, including fuselage, rotors, wings, tails, and propulsion. This information can be the result of the sizing task; can come entirely from input, for a fixed model; or can come from the sizing task in a previous case or previous job. The aircraft description information is available to all tasks and all solutions (indicated by light arrows). The sizing task determines the dimensions, power, and weight of a rotorcraft that can perform a specified set of design conditions and missions. The aircraft size is characterized by parameters such as design gross weight, weight empty, rotor radius, and engine power available. The relations between dimensions, power, and weight generally require an iterative solution. From the design flight conditions and missions, the task can determine the total engine power or the rotor radius (or both power and radius can be fixed), as well as the design gross weight, maximum takeoff weight, drive system torque limit, and fuel tank capacity. For each propulsion group, the engine power or the rotor radius can be sized. Missions are defined for the sizing task, and for the mission performance analysis. A mission consists of a number of mission segments, for which time, distance, and fuel burn are evaluated. For the sizing task, certain missions are designated to be used for design gross weight calculations; for transmission sizing; and for fuel tank sizing. The mission parameters include mission takeoff gross weight and useful load. For specified takeoff fuel weight with adjustable segments, the mission time or distance is adjusted so the fuel required for the mission (burned plus reserve) equals the takeoff fuel weight. The mission iteration is on fuel weight or energy. Flight conditions are specified for the sizing task, and for the flight performance analysis. For the sizing task, certain flight conditions are designated to be used for design gross weight calculations; for transmission sizing; for maximum takeoff weight calculations; and for antitorque or auxiliary thrust rotor sizing. The flight condition parameters include gross weight and useful load. For flight conditions and mission takeoff, the gross weight can be maximized, such that the power required equals the power available. A flight state is defined for each mission segment and each flight condition. The aircraft performance can be analyzed for the specified state, or a maximum effort performance can be identified. The maximum effort is specified in terms of a quantity such as best endurance or best range, and a variable such as speed, rate of climb, or altitude. The aircraft must be trimmed, by solving for the controls and motion that produce equilibrium in the specified flight state. Different trim solution definitions are required for various flight states. Evaluating the rotor hub forces may require solution of the blade flap equations of motion.

NDARC↗
NASA NTRS2023

NDARC - NASA Design and Analysis of Rotorcraft: Input - Appendix 3

The NDARC code performs design and analysis tasks. The design task involves sizing the rotorcraft to satisfy specified design conditions and missions. The analysis tasks can include off-design mission performance analysis, flight performance calculation for point operating conditions, and generation of subsystem or component performance maps. Figure 1-1 illustrates the tasks. The principal tasks (sizing, mission analysis, flight performance analysis) are shown in the figure as boxes with heavy borders. Heavy arrows show control of subordinate tasks. The aircraft description (figure 1-1) consists of all the information, input and derived, that defines the aircraft. The aircraft consists of a set of components, including fuselage, rotors, wings, tails, and propulsion. This information can be the result of the sizing task; can come entirely from input, for a fixed model; or can come from the sizing task in a previous case or previous job. The aircraft description information is available to all tasks and all solutions (indicated by light arrows). The sizing task determines the dimensions, power, and weight of a rotorcraft that can perform a specified set of design conditions and missions. The aircraft size is characterized by parameters such as design gross weight, weight empty, rotor radius, and engine power available. The relations between dimensions, power, and weight generally require an iterative solution. From the design flight conditions and missions, the task can determine the total engine power or the rotor radius (or both power and radius can be fixed), as well as the design gross weight, maximum takeoff weight, drive system torque limit, and fuel tank capacity. For each propulsion group, the engine power or the rotor radius can be sized. Missions are defined for the sizing task, and for the mission performance analysis. A mission consists of a number of mission segments, for which time, distance, and fuel burn are evaluated. For the sizing task, certain missions are designated to be used for design gross weight calculations; for transmission sizing; and for fuel tank sizing. The mission parameters include mission takeoff gross weight and useful load. For specified takeoff fuel weight with adjustable segments, the mission time or distance is adjusted so the fuel required for the mission (burned plus reserve) equals the takeoff fuel weight. The mission iteration is on fuel weight or energy. Flight conditions are specified for the sizing task, and for the flight performance analysis. For the sizing task, certain flight conditions are designated to be used for design gross weight calculations; for transmission sizing; for maximum takeoff weight calculations; and for antitorque or auxiliary thrust rotor sizing. The flight condition parameters include gross weight and useful load. For flight conditions and mission takeoff, the gross weight can be maximized, such that the power required equals the power available. A flight state is defined for each mission segment and each flight condition. The aircraft performance can be analyzed for the specified state, or a maximum effort performance can be identified. The maximum effort is specified in terms of a quantity such as best endurance or best range, and a variable such as speed, rate of climb, or altitude. The aircraft must be trimmed, by solving for the controls and motion that produce equilibrium in the specified flight state. Different trim solution definitions are required for various flight states. Evaluating the rotor hub forces may require solution of the blade flap equations of motion.

NDARC↗
NASA NTRSDate not supplied

Real-Time Pilot Inputs for Aircraft Dynamic Modeling

Real-time dynamic modeling and pilot displays are used to enable pilots to intuitively discover effective multi-axis inputs for aircraft dynamic modeling during flight tests. An F-16 nonlinear simulation flown by a pilot using a joystick and a laptop computer with real-time displays of relevant information is used to demonstrate the approach. Results show that pilots can use the real-time display to discover and implement effective and efficient multi-axis inputs for accurate dynamic modeling in flight. The real-time dynamic modeling results driving the pilot display can be used to certify that the acquired flight data are adequate for accurate dynamic modeling, thus avoiding postflight data analysis and modeling by an analyst to make that determination. Implications for pilot training and efficient flight testing are discussed.

Pilot Inputs↗
DOE OSTI2020

Predicted annual biomass input to maintain soil organic carbon under contrasting management

Corn (Zea mays L.) residue is an important source of lignocellulosic feedstocks for meeting the US renewable energy goals. However, there is uncertainty in how much biomass can be sustainably harvested. The objectives of this study were to (i) simulate soil organic carbon (SOC) dynamics in the 0–20, 20–30, and 0–30 cm soil depths for the 2005–2015 study period using CQESTR, a process-based C model; (ii) establish trends in SOC with residue removal and tillage management over the predictive period of 2016–2035; and (iii) recommend practices to maintain SOC stocks in a corn–soybean (Glycine max L. [Merr.]) rotation with corn residue removal in western Minnesota. The residue removal treatments in the conventional tillage since 1995 (CT 95 ) and no tillage since 1995 (NT 95 ) were initiated in 2005, and in 2006 for the no tillage since 2005 (NT 05 ). Corn residue removal treatments were none, cob removal, ~50% removal, and ~75% removal (R0, RCob, R50, and R75, respectively). Only R0 in NT 05 increased SOC (0.32 Mg C ha –1 yr –1 ) stocks in the top 30 cm soil depth by 2035 with annual aboveground biomass input of 6.30 Mg ha –1 yr –1 . It was concluded that only the NT 05 field has the potential to maintain SOC at the current rate of biomass addition, and a minimal annual aboveground C (MSC) input of 3.75 Mg C ha –1 yr –1 is required to maintain SOC until 2035. These simulation results indicate that caution should be used in removing residue especially under conventional tillage.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

NuGraph2 with context-aware inputs: physics-inspired improvements in semantic segmentation

Graph neural networks have recently shown strong promise for event reconstruction tasks in Liquid Argon Time Projection Chambers, yet their performance remains limited for underrepresented classes of particles, such as Michel electrons. In this work, we investigate physics-informed strategies to improve semantic segmentation within the NuGraph2 architecture. We explore three complementary approaches: (i) enriching the input representation with context-aware features derived from detector geometry and track continuity, (ii) introducing auxiliary decoders to capture class-level correlations, and (iii) incorporating energy-based regularization terms motivated by Michel electron energy distributions. Experiments on MicroBooNE public datasets show that physics-inspired feature augmentation yields the largest gains, particularly boosting Michel electron precision and recall by disentangling overlapping latent space regions. In contrast, auxiliary decoders and energy-regularization terms provided limited improvements, partly due to the hit-level nature of NuGraph2, which lacks explicit particle- or event-level representations. Our findings highlight that embedding physics context directly into node-level inputs is more effective than imposing task-specific auxiliary losses, and suggest that future hierarchical architectures such as NuGraph3, with explicit particle- and event-level reasoning, will provide a more natural setting for advanced decoders and physics-based regularization. The code for this work is publicly available on Github at https://github.com/vitorgrizzi/nugraph_phys/tree/main_phys.

Other Experiments↗
DOE OSTI2025

Artificial neural networks estimate evapotranspiration for Miscanthus × giganteus as effectively as empirical model but with fewer inputs

Estimating actual evapotranspiration (ET) is particularly crucial for addressing how vegetation affects the water balance of ecosystems. ET estimation can be complex with empirical models due to their many parameters and reliance on aridity. In contrast, artificial neural networks (ANNs) could potentially estimate ET with fewer and more common meteorological parameters. In this study, we trained two ANNs, one using a feed-forward approach (FFN) and the other a nonlinear auto-regressive network (NARX), to predict ET and compared them to the commonly used empirical model Granger and Gray (GG). We trained our models on a nine-year eddy covariance (EC) dataset for Miscanthu s × giganteus ( M . × giganteus ) from Illinois (UIEF), then tested them using out-of-sample data from both UIEF and a different location in Iowa (SABR) to compare the accuracy of FFN, NARX, and GG models in estimating daily ET. A combination of air temperature (T a ) and solar radiation (R s ) was chosen as inputs due to the highest R 2 for FFN (R 2 = 0.79, 0.81, and 0.79 for training, testing, and validation, respectively) and only T a for NARX (R 2 = 0.70 for out-of-sample validation). The predictive power of the FFN model was superior to the NARX and GG models at the UIEF site (R 2 = 0.84, 0.70, and 0.83 for out-of-sample validation, respectively). Our analysis showed that ANN approaches are as accurate as empirical approaches for estimating ET but use fewer inputs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

In Situ Observation of the Nucleation and Growth of Ferrite Laths in the Heat-Affected Zone of EH36-Mg Shipbuilding Steel Subjected to Different Heat Inputs

The nucleation and growth behaviors of ferrite laths in the heat-affected zone (HAZ) of EH36-Mg shipbuilding steel with different heat inputs were observed in situ by high-temperature confocal scanning laser microscope (CSLM). It was found that ferrite laths prefer to nucleate on the surface of inclusions instead of grain boundaries under the heat input of 120 kJ/cm, while FSPs are easier to form in 210 kJ/cm due to a significantly reduced cooling rate.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Propagation of Input Uncertainties in Numerical Simulations of Laser Powder Bed Fusion

Laser powder bed fusion has the potential of redefining state-of-the-art processing and production methods, but defect formation and inconsistent build quality have limited the implementation of this process. Numerical models are widely used to study this process and predict the formation of these defects. Presently, the uncertainties of model input parameters and thermophysical properties used by these numerical simulations have not been investigated. In the present study, the uncertainty in these input parameters and material properties are quantified for laser powder bed fusion, with and without a simulated powder bed, to determine their influence on the predictive accuracy of an experimentally validated numerical model. Accounting for all possible sources of uncertainty quickly becomes computationally expensive on account of the curse of dimensionality. Uncertainty in laser absorption, solid, and liquid specific heat of the metal were found to have the largest effect on model prediction reliability with or without the use of a powder bed. Results also illustrate that accounting for these three uncertain parameters still captures the majority of model prediction uncertainty. Furthermore, the methodology of this study may be used to understand the uncertainty in as-built microstructure through propagation to microstructure prediction models, or applied under processing conditions where high Péclet numbers are observed and the thermal convection and fluid flow within the molten pool are substantial.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Effects of Heat Input on Weld-Bead Geometry, Surface Chemical Composition, Corrosion Behavior and Thermal Properties of Fiber Laser-Welded Nitinol Shape Memory Alloy

Bead-on-plate laser welding was carried out on 1-mm-thick Nitinol sheets at different heat input values. The variations in weld-bead geometry and variations in microstructures across the bead were studied. The effects of heat input on surface layer composition of the welded samples, corrosion properties and phase transformation temperature were examined. From x-ray photoelectron spectroscopy (XPS), it was clear that relative intensity of elemental Ni peak was higher in parent material and the oxidation state of Ti was Ti{sup 4+}. It was further observed from Auger electron spectroscopy (AES) that the Ti to Ni ratio on the top surface of the welded samples was more than 1, and as a result of this the stability of TiO{sub 2} layer was enhanced, which eventually helped to improve the corrosion property of the welded samples. The polarization resistance of the welded samples was increased by more than ten times compared to that of the un-welded samples. From differential scanning calorimetry (DSC), it became evident that phase transformation temperatures got changed due to welding.

36 MATERIALS SCIENCE↗