Engineering topics

Makaruk, Hanna Ewa

Publications and source records attributed to Makaruk, Hanna Ewa.

8 records

DOE OSTI2022

Topological Invariant of Manifolds in Slawianowski’s Field Theory [Slides]

Witten constructed topological invariants of manifolds by introducing actions depending only on smooth structures of the manifold without using a metric. He focused on an example of Chern-Simons’ theory on 3D manifolds. Slawianowski based his field theory on frame/coframe fields without a metric. A Witten-type topological invariant can be introduced in Slawianowski’s framework, using an integral over the reper fields. Such invariant behaves differently for group manifolds than for all others. It additionally distinguishes semi-simple Lie group manifolds from other group manifolds. Further study of this new invariant is needed, including possible generalizations to other field theories.

42 ENGINEERING↗

DOE OSTI2021

SU(n) and Quantum SU(n) Symmetries in Physical Systems [Slides]

Presence of SU(n) or other Lie group symmetry in a physical system is its powerful, usually underutilized property. In many cases it allows for finding analytical solutions to nonlinear differential equations describing this system. Power of the method is presented on diversified examples from mathematical physics: Lie-group symmetries in finding solutions of generalized, multidimensional theory of gravity; analytical Dirac–equation solutions for description of conducting polymers; stability of qubit states in quantum computers; spatial defects in condensed matter; reconstruction of 3D object from its 2D tomographic image; significant improvement of numerical solutions stability for Euler equations. The next question after obtaining such Lie group symmetric solution is: does a generalized solution with appropriate quantum group symmetry exists for the given physical system, and if yes what is the physical meaning of the deformation parameter q introduced by such solution. In many cases it can be identified. Any SU(n) solution is by its nature singular, assuming a perfect symmetry of the physical system discussed. Such solution gives a powerful insight to theoretical physics, yet the assumption may be too demanding for experimental applications. Deformation parameter q from a quantum group symmetry allows for a continuum of solutions, more applicable to experiments.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2020

Uncertainty in Experimental Data Analysis [Slides]

The talk was presented virtually to the Institute of Fundamental Technological Research, Polish Academy of Sciences in Warsaw, Poland, December 21, 2020. Astronomical observations, unusual medical cases, physics experiments too costly to repeat, natural events like earthquakes, hurricanes – all of them are impossible to repeat yet produce important scientific information not achievable in other way. This data should not be treated as qualitative, anecdotal evidence only. It should be analyzed in a mathematically rigorous way to produce quantitative experimental data. Analysis method for one-of-a-kind event data differs from analysis of a repeated experiment data. For a repeated experiments the experimental error includes a range of true values generated by repetitions of the experiment, and measurement uncertainty caused by detectors. They are independent. Repetitions of any experiment, as similar as achievable, always have built-in differences resulting in a range of the true values rather than in a single true experimental value. Measurement uncertainty depends on the measurement system only. Modern digital measurements have very small uncertainty, frequently smaller than the range of true experimental values resulting from built-in differences in the experiment repetitions. When data from one-of-a-kind experiment are analyzed, only the measurement uncertainty can be reported.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2020

Measurement Uncertainty in One-of-a-kind Experiments

A golden standard in science is to repeat an experimental measurement multiple times and calculate the measured value with experimental uncertainty by following well developed statistical procedures. For various reasons – cost, technical difficulty, international treaties, ethics of dealing with human or animal subjects, ecology - many important experiments and observations can not be repeated. Astronomy, earthquakes, hurricanes produce data from one-of-a-kind events. When information is not available in any other way, it should not be dismissed as qualitative, anecdotal evidence only. Analyzed in a mathematically rigorous way, it produces quantitative experimental data. Analysis of data from one-of-a-kind event differs from analysis of repeated experiments’ data. For repeated experiments, the experimental error includes a range of true values generated by repetitions of the experiment, and measurement uncertainty caused by detectors. They are independent. Repetitions of any experiment, as similar as achievable, always have built-in differences resulting in a range of the true values rather than in a single value. Measurement uncertainty depends on the measurement system only. Digital measurements have very small uncertainty, frequently smaller than the range of true experimental values resulting from built-in differences in the experiment repetitions. When data from one–of–a kind experiment are analyzed, only the measurement uncertainty can be reported.

42 ENGINEERING↗

DOE OSTI2020

Measurement Uncertainty in One-Of-A-Kind Event Data Analysis

A golden standard in science is to repeat an experiment a statistically significant number of times, recording data using the same set of detectors and the same data analysis methodology. In such case experimental error includes both the range of true values generated by repetitions of the experiment, and measurement uncertainty caused by the detector. They are independent. It is a huge and too frequently used simplification, to assume that one can measure multiple repetitions of an identical experiment, resulting in identical true experimental value. Repetitions, as similar is it is experimentally achievable, have unavoidable built-in differences resulting in a range of the true values rather than in a single value. When modern, very sensitive and well calibrated measurement systems are used, this range is not negligible, and sometimes dominates over the measurement uncertainty. Range of true values depends on built-in differences in physics of the experiment. Stochastic physical processes result typically in a broader range of true values than non-stochastic processes do. Measurement uncertainty depends on a measurement method (properties of the detector not of the experiment). Modern measurement methods, including digital ones, frequently make the measurement uncertainty very small. When data from one–of –a kind experiment are analyzed, only the measurement uncertainty is reported. It provides no information about the range of true experimental values, neither about reliability of a reported data point. Reliability of a data point is in general independent from its measurement uncertainty. However, in practice reliable measurement methods frequently have high measurement uncertainty, while low reliability methods are applied to limit measurement uncertainty. Comparison of reliable data with high measurement uncertainty to not so reliable data measured with low uncertainty is discussed – in different scenarios different data analysis methods are applicable. Methods for data analysis from an experiment repeated statistically significant number of times are very well developed. They do not require a detailed expertise in physics of an experiment, nor in the properties of the measurement system used, and meaning of the reported uncertainty is well understood in any scientific community. It all changes when data from one-of-a-kind experiment is analyzed. Analyst’s expertise is required both in the physics of the experiment and in all aspects of the measurement system, all possible malfunctions. Data users must remember that only measurement uncertainty is reported from any one-of-a-kind experiment. Theory with simulations may provide estimation of expected built-in differences in the experiment, and by this of expected range of true values for a given experiment; yet measurement uncertainty can never be used in place of the range of true experimental values.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2020

Reaction History Uncertainty Propagation from Flux to α and from Time to α [Slides]

We discuss uncertainty propagation in reaction history data from underground nuclear tests. γ reaction history detectors measured flux as a function of time. After data analysis we report α as a function of time. Flux uncertainty and time uncertainty propagation into α uncertainty are discussed in detail. Discretization of the formulas is discussed.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2020

Uncertainty about the Uncertainty [Slides]

A golden standard in science is to repeat an experiment a statistically significant number of times, recording data using the same set of detectors and the same data analysis methodology. In such case experimental error includes both the range of true values generated by repetitions of the experiment, and measurement uncertainty caused by the detector. They are independent. It is a huge and too frequently used simplification, to assume that one can measure multiple repetitions of an identical experiment, resulting in identical true experimental value. Repetitions, as similar is it is experimentally achievable result in a range of the true values rather than in a single value. When modern, very sensitive and well calibrated measurement systems are used, this range is not negligible, and sometimes dominates, in comparison to the measurement uncertainty. Range of true values depends on the physics of the experiment, while measurement uncertainty depends on the measurement method (properties of the detector not of the experiment). When data from one–of–a kind experiment are analyzed, only the measurement uncertainty is reported. It gives no information about the range, in which the true values of experiment would spread if the experiment was repeated. A frequently used approximation, that if a physical quantity is measured as a function of time, only measurement of this quantity, produces uncertainty is also in some real experiments fare to strong. Example: In reaction history time measurement uncertainty propagated to alpha dominated under certain conditions over the flux measurement uncertainty propagated to alpha. Reliability of a data point is in general independent from its measurement uncertainty. However, in practice reliable measurement methods frequently have high measurement uncertainty, while low reliability methods are applied to limit measurement uncertainty. Comparison of reliable data with high measurement uncertainty to not so reliable data measured with low uncertainty is discussed – in different scenarios different data analysis methods are applicable.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2020

Uncertainty Propagation from Flux to α and from Time to α in Reaction History

This report documents measurement uncertainty propagation from flux to α and from time to α in gamma reaction history. Analytical formulas are derived, and on the basis of them, their discrete versions are provided to serve for current reaction history software verification, and for development of the future versions of the software. The discrete version is provided for the general case: sparse data with varying time intervals between the data points.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗