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Refinement of the Loran-C receiver system for general aviation use was described. The capability to provide area navigation sometimes called random navigation (RNAV) was accomplished by providing new software. Microcomputers were used to accomplish the calculations which were performed in flight to demonstrate these devices are cost effective in the implementation of Loran-C navigational information.
The results of a second year effort of a research program are presented. The research included development of methodology that provides probabilistic lifetime strength of aerospace materials via computational simulation. A probabilistic phenomenological constitutive relationship, in the form of a randomized multifactor interaction equation, is postulated for strength degradation of structural components of aerospace propulsion systems subjected to a number of effects of primitive variables. These primitive variables often originate in the environment and may include stress from loading, temperature, chemical, or radiation attack. This multifactor interaction constitutive equation is included in the computer program, PROMISS. Also included in the research is the development of methodology to calibrate the constitutive equation using actual experimental materials data together with the multiple linear regression of that data.
Direct numerical simulations with initial small-amplitude random disturbances are used to investigate instability and transition in plane Pouseuille flow with spanwise system rotation, employing a large computational domain to facilitate the selection of the dominant spanwise wavelength of the observed streamwise vortices as well as the streamwise wavelength of the dominant secondary modes due to streamwise vortices. In all cases studied, the unstable primary three-dimensional waves persist in the nonlinear stage. Where there is primary three-dimensional wave decay, the finite-amplitude streamwise vortices can become unstable to two different three-dimensional secondary instability modes, depending on the Reynolds and the rotation numbers.
The properties of simple, self-consistent infall models which embed an 'isothermal' dark halo in an open universe are presented. These models depend on one scaling parameter and two shape parameters. The first may be taken to be the circular velocity in the inner regions, and the others to be the density of the universe, Omega, and a measure, e, of the shape of orbits. These models provide a useful description of ensembles of satellite galaxies, or of binary galaxies, because they allow explicitly for the fact that such systems have orbital periods approaching the age of the universe and so cannot have a random orbital phase.
Rock Identification Toolkit Suite is a computer program that assists users in identifying and characterizing rocks shown in images returned by the Mars Explorer Rover mission. Included in the program are components for automated finding of rocks, interactive adjustments of outlines of rocks, active contouring of rocks, and automated analysis of shapes in two dimensions. The program assists users in evaluating the surface properties of rocks and soil and reports basic properties of rocks. The program requires either the Mac OS X operating system running on a G4 (or more capable) processor or a Linux operating system running on a Pentium (or more capable) processor, plus at least 128MB of random-access memory.
Changes in urine chemistry, during and post flight, potentially increases the risk of renal stones in astronauts. Although much is known about the effects of space flight on urine chemistry, no inflight incidence of renal stones in US astronauts exists and the question "How much does this risk change with space flight?" remains difficult to accurately quantify. In this discussion, we tackle this question utilizing a combination of deterministic and probabilistic modeling that implements the physics behind free stone growth and agglomeration, speciation of urine chemistry and published observations of population renal stone incidences to estimate changes in the rate of renal stone presentation. The modeling process utilizes a Population Balance Equation based model developed in the companion IWS abstract by Kassemi et al. (2016) to evaluate the maximum growth and agglomeration potential from a specified set of urine chemistry values. Changes in renal stone occurrence rates are obtained from this model in a probabilistic simulation that interrogates the range of possible urine chemistries using Monte Carlo techniques. Subsequently, each randomly sampled urine chemistry undergoes speciation analysis using the well-established Joint Expert Speciation System (JESS) code to calculate critical values, such as ionic strength and relative supersaturation. The Kassemi model utilizes this information to predict the mean and maximum stone size. We close the assessment loop by using a transfer function that estimates the rate of stone formation from combining the relative supersaturation and both the mean and maximum free stone growth sizes. The transfer function is established by a simulation analysis which combines population stone formation rates and Poisson regression. Training this transfer function requires using the output of the aforementioned assessment steps with inputs from known non-stone-former and known stone-former urine chemistries. Established in a Monte Carlo system, the entire renal stone analysis model produces a probability distribution of the stone formation rate and an expected uncertainty in the estimate. The utility of this analysis will be demonstrated by showing the change in renal stone occurrence predicted by this method using urine chemistry distributions published in Whitson et al. 2009. A comparison to the model predictions to previous assessments of renal stone risk will be used to illustrate initial validation of the model.
A novel extension of the Probabilistic Learning on Manifolds (PLoM) is presented. It makes it possible to synthesize solutions to a wide range of nonlinear stochastic boundary value problems described by partial differential equations (PDEs) for which a stochastic computational model (SCM) is available and which depend on a vector-valued random control parameter. The cost of a single numerical evaluation of this SCM is assumed to be such that only a limited number of points can be computed for constructing the training dataset (small data). Each point of the training dataset is made up of realizations from a vector-valued stochastic process (the stochastic solution) and the associated random control parameter on which it depends. The presented PLoM constrained by PDE allows for generating a large number of learned realizations of the stochastic process and its corresponding random control parameter. These learned realizations are generated so as to minimize the vector-valued random residual of the PDE in the mean-square sense. Appropriate novel methods are developed to solve this challenging problem. Three applications are presented. The first one is a simple uncertain nonlinear dynamical system with a nonstationary stochastic excitation. The second one concerns the 2D nonlinear unsteady Navier–Stokes equations for incompressible flows in which the Reynolds number is the random control parameter. Here, the last one deals with the nonlinear dynamics of a 3D elastic structure with uncertainties. The results obtained make it possible to validate the PLoM constrained by stochastic PDE but also provide further validation of the PLoM without constraint.
The preliminary system definition study for an Astrometric Telescope Facility (ATF) designed for the Space Station IOC is discussed, and a strawman system is designed which is found to meet the requirements for extrasolar planetary systems search and study. The strawman facility design, with a prime-focus 1.25-m aperture telescope and an f ratio of 13, was selected to minimize random and systematic errors. A basic operations approach is identified, including the approach to launch, initial on-orbit assembly and checkout, normal operations, and the response to anomolous conditions or failures. The preliminary system is designed to be fail-safe and single-fault tolerant. Mission analysis indicates that the basic viewing required for planetary detection can be accomplished in about 2/3 of the total viewing time.
In order to study mechanisms of scattering and their relative contributions to synthetic aperture radar (SAR) and inverse SAR images, a special measurement facility has been constructed. It can be used to generate images in which the target is illuminated from a constant direction as the receiver is rotated in azimuth, and the target is illuminated from various directions as the receiver is rotated in azimuth. The imaging system performance is discussed, and images obtained using both approaches are compared, for a variety of simple targets and statistically known randomly rough surfaces.
Stochastic dynamical systems arise naturally across nearly all areas of science and engineering. Typically, a dynamical system model is based on some prior knowledge about the underlying dynamics of interest in which probabilistic features are used to quantify and propagate uncertainties associated with the initial conditions, external excitations, etc. From a probabilistic modeling standing point, two broad classes of methods exist, i.e. macro-scale methods and micro-scale methods. Classically, macro-scale methods such as statistical moments-based strategies are usually too coarse to capture the multi-mode shape or tails of a non-Gaussian distribution. Micro-scale methods such as random samples-based approaches, on the other hand, become computationally very challenging in dealing with high-dimensional stochastic systems. In view of these potential limitations, a meso-scale scheme is proposed here that utilizes a meso-scale statistical structure to describe the dynamical evolution from a probabilistic perspective. The significance of this statistical structure is twofold. First, it can be tailored to any arbitrary random space. Second, it not only maintains the probability evolution around sample trajectories but also requires fewer meso-scale components than the micro-scale samples. To demonstrate the efficacy of the proposed meso-scale scheme, a set of examples of increasing complexity are provided. Connections to the benchmark stochastic models as conservative and Markov models along with practical implementation guidelines are presented.
We use the concepts of statistical electromagnetics to derive the general radiative transfer equation (RTE) describing multiple scattering of polarized light by sparse discrete random media consisting of arbitrarily shaped and arbitrarily oriented particles. The derivation starts from the volume integral and Lippmann-Schwinger equations for the electric field scattered by a fixed N-particle system and proceeds to the vector form of the Foldy-Lax equations and their approximate far-field version. We then assume that particle positions are completely random and derive the vector RTE by applying the Twersky approximation to the coherent electric field and the Twersky and ladder approximations to the coherency dyad of the diffuse field in the limit N -> infinity. The concluding section discusses the physical meaning of the quantities entering the general vector RTE and the assumptions made in its derivation.
With the growing scarcity of freshwater, innovative process design mechanisms like Ultra-filtration(UF) units are increasingly gaining attention among water treatment utilities to address the rising demand. Ensuring reliable water production necessitates efficient resource utilization, minimizing downtime in UF systems. Recent advancements in machine learning (ML) have enabled the development of accurate data-driven models for Model Predictive Control (MPC), often requiring minimal prior knowledge of underlying physical processes. In this study, we present predictive regression models based on Random Forest (RF) and Auto-Regressive (AR) approaches to forecast the initial Trans-Membrane Pressure (TMP) for each filtration cycle in data generated by Direct Potable Reuse (DPR) systems. The proposed RF-based model demonstrates superior performance compared to baseline methods, including historical mean, Last Observation Carried Forward (LOCF), and naïve AR models, across various forecasting horizons in terms of root mean square (RMSE) metric. Accurate prediction of initial TMP is critical for optimizing CCRO operations, as it enables the development of robust modelling frameworks that enhance process efficiency and reliability. The demonstrated efficacy of the RF-based approach highlights its potential as a tool for real-time decision-making in water treatment systems, paving the way for advanced process optimization and sustainable water resource management.
The capability to vent in zero gravity without resettling is a technology need that involves practically all uses of sub-critical cryogenics in space. Venting without resettling would extend cryogenic orbital transfer vehicle capabilities. However, the lack of definition regarding liquid/ullage orientation coupled with the somewhat random nature of the thermal stratification and resulting pressure rise rates, lead to significant technical challenges. Typically a zero gravity vent concept, termed a thermodynamic vent system (TVS), consists of a tank mixer to destratify the propellant, combined with a Joule-Thomson (J-T) valve to extract thermal energy from the propellant. Marshall Space Flight Center's (MSFC's) Multipurpose Hydrogen Test Bed (MHTB) was used to test both spray bar and axial jet TVS concepts. The axial jet system consists of a recirculation pump heat exchanger unit. The spray bar system consists of a recirculation pump, a parallel flow concentric tube, heat exchanger, and a spray bar positioned close to the longitudinal axis of the tank. The operation of both concepts is similar. In the mixing mode, the recirculation pump withdraws liquid from the tank and sprays it into the tank liquid, ullage, and exposed tank surfaces. When energy is required. a small portion of the recirculated liquid is passed sequentially through the J-T expansion valve, the heat exchanger, and is vented overboard. The vented vapor cools the circulated bulk fluid, thereby removing thermal energy and reducing tank pressure. The pump operates alone, cycling on and off, to destratify the tank liquid and ullage until the liquid vapor pressure reaches the lower set point. At that point. the J-T valve begins to cycle on and off with the pump. Thus, for short duration missions, only the mixer may operate, thus minimizing or even eliminating, boil-off losses.
Both computational and experimental material discovery bring forth the challenge of exploring multidimensional and often nondifferentiable parameter spaces, such as phase diagrams of Hamiltonians with multiple interactions, composition spaces of combinatorial libraries, processing spaces, and molecular embedding spaces. Often these systems are expensive or time consuming to evaluate a single instance, and hence classical approaches based on exhaustive grid or random search are too data intensive. This resulted in strong interest toward active learning methods such as Bayesian optimization (BO) where the adaptive exploration occurs based on human learning (discovery) objective. However, classical BO is based on a predefined optimization target, and policies balancing exploration and exploitation are purely data driven. In practical settings, the domain expert can pose prior knowledge of the system in the form of partially known physics laws and exploration policies often vary during the experiment. Here, we propose an interactive workflow building on multifidelity BO (MFBO), starting with classical (data-driven) MFBO, then expand to a proposed structured (physics-driven) structured MFBO (sMFBO), and finally extend it to allow human-in-the-loop interactive interactive MFBO (iMFBO) workflows for adaptive and domain expert aligned exploration. These approaches are demonstrated over highly nonsmooth multifidelity simulation data generated from an Ising model, considering spin–spin interaction as parameter space, lattice sizes as fidelity spaces, and the objective as maximizing heat capacity. Detailed analysis and comparison show the impact of physics knowledge injection and real-time human decisions for improved exploration with increased alignment to ground truth. Here, the associated notebooks allow to reproduce the reported analyses and apply them to other systems.
Wind energy is a crucial technology for achieving net-zero emissions by 2050. However, the growth and deployment of wind energy in North America have led to the deaths of many bat species due to operating wind turbines. Hundreds of thousands of bats are estimated to die at wind turbines annually in North America. Operational minimization, which includes feathering turbine blades and curtailment, has been documented to reduce bat fatality effectively. Curtailment refers to altering turbine operation based on wind speed, time of year, temperature, sensors, and activity models. However, when turbines are curtailed, they do not generate power, resulting in energy loss and revenue for wind energy facilities. The Electric Power Research Institute (EPRI) funded the development of Turbine Integrated Mortality Reduction (TIM SM ) Technology, which curtails turbine operation when bats are detected. The initial TIMR system research showed promising results, with an 85% reduction in overall bat fatalities and a 91% reduction for the little brown bat. However, these results were based on a single site during one fall season, and it was unclear if similar results could be replicated at other wind energy facilities. This research aimed to validate the TIMR system results from the prior field study at a second site in the U.S., estimate the power production and reduction in bat mortality at turbines with installed TIMR systems relative to blanket curtailment and fully operational turbines, test the TIMR system in two calendar years and during the summer and fall periods, and evaluate the operational and commercial characteristics of the TIMR system for potential wind industry adoption. The study was conducted at a 500.9-MW wind energy facility in southeast Adair County, Iowa. Three experimental treatments were involved in this randomized block design study: TIMR, Curtailment at 5.0 m/s, and Normal Operation. In 2021, three treatments were used at 18 turbines, expanding to four treatments across 36 turbines in 2022. The TIMR system worked as designed throughout the entire study; however, because of unexpected wind turbine operational challenges in 2021, there was not sufficient sample size to evaluate the treatment differences. In 2022, there were significant differences in fatality levels between treatment types and normal operating turbines. Curtailment at 5.0 m/s reduced fatalities by 30.8% compared to normal operations, and TIMR decreased fatalities by 48.6% compared to normal operations. Two different methods were used to evaluate the differences in energy loss for each treatment. The TIMR system resulted in 1.3% to 1.6 % annual energy loss in 2021 and 1.0% to 1.2 % in 2022. The Curtailment at 5.0 m/s resulted in 0.6% to 0.8 % annual energy loss in 2021 and 0.5% to 0.6 % in 2022. The project achieved all the stated objectives and demonstrated that TIMR is an effective technology that balances bat fatality reduction with energy generation. The results will support the deployment of TIMR and other acoustic sensor-based technologies. The research provides valuable insights into the impact of different treatments on fatality rates and energy outputs, contributing to the ongoing efforts to mitigate the environmental impact of wind energy.
At two sites in the North Central USA (Michigan (KBS) and Wisconsin (ARL)), we evaluated the effect of N fertilization on the yield and quality of five perennial bioenergy feedstock cropping systems: (1) switchgrass (Panicum virgatum L.), (2) giant miscanthus (Miscanthus × giganteus), (3) a native grass mixture (5 species), (4) an early successional field (volunteer herbaceous species), and (5) a restored prairie (18 species). In a randomized complete block design with 5 replicates and 2 split plots, N was applied at 0 and 56 kg ha –1 to split plots for each cropping system from 2010 to 2016. No yield response to N was detected in switchgrass at either location in any year. Giant miscanthus exhibited a positive yield response to N at both sites (11% at KBS and 83% at ARL). Nitrogen fertilizer addition significantly reduced glucose (KBS 12.9 and 13.8 g kg –1 year –1 , ARL 11.2 and 9.7 g kg –1 year –1 ) in the native grass mix and restored prairie systems respectively. Here, nitrogen fertilizer also reduced xylose at KBS in the switchgrasss, native grass mix, and restored prairie (4.9, 7.5, and 5.0 g kg –1 year –1 ). At ARL, N fertilization reduced xylose levels in switchgrass, giant miscanthus, and restored prairie (7.4, 6.8, and 6.2 g kg –1 year –1 ) and increased xylose levels in the early successional system (5.0 g kg –1 year –1 ).
With the growing scarcity of freshwater, innovative process design mechanisms like Reverse Osmosis (RO) are increasingly gaining attention among water treatment utilities to address the rising demand. Ensuring reliable water production necessitates efficient resource utilization, minimizing downtime in (ultra-filtration) UF systems. Recent advancements in machine learning (ML) have enabled the development of accurate data-driven models for Model Predictive Control (MPC), often requiring minimal prior knowledge of underlying physical processes. In this study, we present predictive regression models based on Random Forest (RF) and Auto-Regressive (AR) approaches to forecast the initial Trans-Membrane Pressure (TMP) for each filtration cycle in data generated by Direct Potable Reuse (DPR) systems. The proposed RF-based model demonstrates superior performance compared to baseline methods, including historical mean, Last Observation Carried Forward (LOCF), and naïve AR models, across various forecasting horizons in terms of root mean square error (RMSE) metric. To evaluate how different classes of process variables contribute to TMP dynamics over time, we examine the feature importance of independent covariates across multiple forecast horizons. This analysis provides insight into the temporal relevance of operational and sensor-derived features, guiding control and monitoring strategies. Additionally, the impact of hyperparameter tuning on TMP prediction performance is studied for both direct and recursive RF modelling approaches across increasing forecast horizons. Accurate prediction of initial TMP is critical for optimizing RO operations, as it enables the development of robust modelling frameworks by accurately estimating membrane fouling trends, thereby enhancing process efficiency and long-term reliability. The demonstrated efficacy of the RF-based approach highlights its potential as a tool for real-time decision-making in water treatment systems, paving the way for advanced process optimization and sustainable water resource management.
The author has identified the following significant results. The concept of average mutual information in the received spectral random process about the spectral scene was developed. Techniques amenable to implementation on a digital computer were also developed to make the required average mutual information calculations. These techniques required identification of models for the spectral response process of scenes. Stochastic modeling techniques were adapted for use. These techniques were demonstrated on empirical data from wheat and vegetation scenes.